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A Scalable Approach for Partially Local Federated Learning

Posted by Karan Singhal, Senior Software Engineer, Google Research

Federated learning enables users to train a model without sending raw data to a central server, thus avoiding the collection of privacy-sensitive data. Often this is done by learning a single global model for all users, even though the users may differ in their data distributions. For example, users of a mobile keyboard application may collaborate to train a suggestion model but have different preferences for the suggestions. This heterogeneity has motivated algorithms that can personalize a global model for each user.

However, in some settings privacy considerations may prohibit learning a fully global model. Consider models with user-specific embeddings, such as matrix factorization models for recommender systems. Training a fully global federated model would involve sending user embedding updates to a central server, which could potentially reveal the preferences encoded in the embeddings. Even for models without user-specific embeddings, having some parameters be completely local to user devices would reduce server-client communication and responsibly personalize those parameters to each user.

Left: A matrix factorization model with a user matrix P and items matrix Q. The user embedding for a user u (Pu) and item embedding for item i (Qi) are trained to predict the user’s rating for that item (Rui). Right: Applying federated learning approaches to learn a global model can involve sending updates for Pu to a central server, potentially leaking individual user preferences.

In “Federated Reconstruction: Partially Local Federated Learning”, presented at NeurIPS 2021, we introduce an approach that enables scalable partially local federated learning, where some model parameters are never aggregated on the server. For matrix factorization, this approach trains a recommender model while keeping user embeddings local to each user device. For other models, this approach trains a portion of the model to be completely personal for each user while avoiding communication of these parameters. We successfully deployed partially local federated learning to Gboard, resulting in better recommendations for hundreds of millions of keyboard users. We’re also releasing a TensorFlow Federated tutorial demonstrating how to use Federated Reconstruction.

Federated Reconstruction
Previous approaches for partially local federated learning used stateful algorithms, which require user devices to store a state across rounds of federated training. Specifically, these approaches required devices to store local parameters across rounds. However, these algorithms tend to degrade in large-scale federated learning settings. In these cases, the majority of users do not participate in training, and users who do participate likely only do so once, resulting in a state that is rarely available and can get stale across rounds. Also, all users who do not participate are left without trained local parameters, preventing practical applications.

Federated Reconstruction is stateless and avoids the need for user devices to store local parameters by reconstructing them whenever needed. When a user participates in training, before updating any globally aggregated model parameters, they randomly initialize and train their local parameters using gradient descent on local data with global parameters frozen. They can then calculate updates to global parameters with local parameters frozen. A round of Federated Reconstruction training is depicted below.

Models are partitioned into global and local parameters. For each round of Federated Reconstruction training: (1) The server sends the current global parameters g to each user i; (2) Each user i freezes g and reconstructs their local parameters li; (3) Each user i freezes li and updates g to produce gi; (4) Users’ gi are averaged to produce the global parameters for the next round. Steps (2) and (3) generally use distinct parts of the local data.

This simple approach avoids the challenges of previous methods. It does not assume users have a state from previous rounds of training, enabling large-scale training, and local parameters are always freshly reconstructed, preventing staleness. Users unseen during training can still get trained models and perform inference by simply reconstructing local parameters using local data.

Federated Reconstruction trains better performing models for unseen users compared to other approaches. For a matrix factorization task with unseen users, the approach significantly outperforms both centralized training and baseline Federated Averaging.

RMSE ↓ Accuracy ↑
Centralized 1.36 40.8%
FedAvg .934 40.0%
FedRecon (this work) .907 43.3%
Root-mean-square-error (lower is better) and accuracy for a matrix factorization task with unseen users. Centralized training and Federated Averaging (FedAvg) both reveal privacy-sensitive user embeddings to a central server, while Federated Reconstruction (FedRecon) avoids this.

These results can be explained via a connection to meta learning (i.e., learning to learn); Federated Reconstruction trains global parameters that lead to fast and accurate reconstruction of local parameters for unseen users. That is, Federated Reconstruction is learning to learn local parameters. In practice, we observe that just one gradient descent step can yield successful reconstruction, even for models with about one million local parameters.

Federated Reconstruction also provides a way to personalize models for heterogeneous users while reducing communication of model parameters — even for models without user-specific embeddings. To evaluate this, we apply Federated Reconstruction to personalize a next word prediction language model and observe a substantial increase in performance, attaining accuracy on par with other personalization methods despite reduced communication. Federated Reconstruction also outperforms other personalization methods when executed at a fixed communication level.

Accuracy ↑ Communication ↓
FedYogi 24.3% Whole Model
FedYogi + Finetuning 30.8% Whole Model
FedRecon (this work) 30.7% Partial Model
Accuracy and server-client communication for a next word prediction task without user-specific embeddings. FedYogi communicates all model parameters, while FedRecon avoids this.

Real-World Deployment in Gboard
To validate the practicality of Federated Reconstruction in large-scale settings, we deployed the algorithm to Gboard, a mobile keyboard application with hundreds of millions of users. Gboard users use expressions (e.g., GIFs, stickers) to communicate with others. Users have highly heterogeneous preferences for these expressions, making the setting a good fit for using matrix factorization to predict new expressions a user might want to share.

Gboard users can communicate with expressions, preferences for which are highly personal.

We trained a matrix factorization model over user-expression co-occurrences using Federated Reconstruction, keeping user embeddings local to each Gboard user. We then deployed the model to Gboard users, leading to a 29.3% increase in click-through-rate for expression recommendations. Since most Gboard users were unseen during federated training, Federated Reconstruction played a key role in this deployment.

Further Explorations
We’ve presented Federated Reconstruction, a method for partially local federated learning. Federated Reconstruction enables personalization to heterogeneous users while reducing communication of privacy-sensitive parameters. We scaled the approach to Gboard in alignment with our AI Principles, improving recommendations for hundreds of millions of users.

For a technical walkthrough of Federated Reconstruction for matrix factorization, check out the TensorFlow Federated tutorial. We’ve also released general-purpose TensorFlow Federated libraries and open-source code for running experiments.

Acknowledgements
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash co-authored the paper. Thanks to Wei Li, Matt Newton, and Yang Lu for their partnership on Gboard deployment. We’d also like to thank Brendan McMahan, Lin Ning, Zachary Charles, Warren Morningstar, Daniel Ramage, Jakub Konecný, Alex Ingerman, Blaise Agüera y Arcas, Jay Yagnik, Bradley Green, and Ewa Dominowska for their helpful comments and support.

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Omniverse Creator Uses AI to Make Scenes With Singing Digital Humans

The thing about inspiration is you never know where it might come from, or where it might lead. Anderson Rohr, a 3D generalist and freelance video editor based in southern Brazil, has for more than a dozen years created content ranging from wedding videos to cinematic animation. After seeing another creator animate a sci-fi character’s Read article >

The post Omniverse Creator Uses AI to Make Scenes With Singing Digital Humans appeared first on The Official NVIDIA Blog.

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NVIDIA to Unveil Latest Accelerated Computing Breakthroughs in Virtual Special Address During CES

From Design and Simulation to Gaming to Autonomous VehiclesSANTA CLARA, Calif., Dec. 16, 2021 (GLOBE NEWSWIRE) — NVIDIA today announced that it will deliver a special address during CES on …

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Get the Best of Cloud Gaming With GeForce NOW RTX 3080 Memberships Available Instantly

The future of cloud gaming is available NOW, for everyone, with preorders closing and GeForce NOW RTX 3080 memberships moving to instant access. Gamers can sign up for a six-month GeForce NOW RTX 3080 membership and instantly stream the next generation of cloud gaming, starting today. Snag the NVIDIA SHIELD TV or SHIELD TV Pro Read article >

The post Get the Best of Cloud Gaming With GeForce NOW RTX 3080 Memberships Available Instantly appeared first on The Official NVIDIA Blog.

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Forecast of a time series with tensorflow and neural network

hi, in the time series of the following notebook:

https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C4/W2/ungraded_labs/C4_W2_Lab_2_single_layer_NN.ipynb

I understood how to analyze the train set and the validation set with a neural network with tensorflow but I did not understand how to analyze the testing set. I wrote the following code to analyze it. Can you tell me if it’s right? thank you

forecast = [] n=len(series) time_test = np.arange(n, n+365-window_size, dtype=”float32″)

for time in range(n, n+365 – window_size): pred=model.predict(series[time-window_size:time ][np.newaxis]) forecast.append(pred) series=np.append(series,pred)

results = np.array(forecast)[:, 0, 0]

plt.figure(figsize=(10, 6))

plot_series(time_valid, x_valid) plot_series(time_test, results)</code>

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NLP – How to get correlated words?

Hi everyone, I’m not an expert of tensorflow, I’ve only used some pretrained api of Tensorflow.js.

I need to get correlated words given a specific word, example: 

Input: "banana" Output: "fruit, market, yellow"

I tried with GPT-3 playground and given a template it’s really good at this, but it looks like I’m trying to shoot a fly with a tank…

Do you know any pretrained-model or maybe a specific api that can help with this?

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NVIDIA AI: Generating Motion Capture Animation Without Hardware or Motion Data

A graphic showing a person turned into an avatar that is trying to walk in a snow storm.NVIDIA researchers developed a framework to build motion capture animation without the use of hardware or motion data by simply using video capture and AI.A graphic showing a person turned into an avatar that is trying to walk in a snow storm.

Researchers from NVIDIA, the University of Toronto, and the Vector Institute have proposed a new motion capture method foregoing the use of expensive motion-capture hardware. It uses only video input to improve past motion-capture animation models. 

YouTuber and graphics researcher Dr. Károly Zsolnai-Fehér breaks down the research on this innovative technology in his YouTube series: Two Minute Papers. This video highlights how the researchers can capture individuals using AI solely through video input to translate it into a digital avatar. They can then give the avatar a physics simulation to negate the conventional challenges of foot sliding and temporal inconsistencies or flickering. Check out the video below:


Figure 1: A video presenting the paper “Physics-based Human Motion Estimation and Synthesis from Videos in 2 minutes.”

“In this paper, we introduced a new framework for training motion synthesis models from raw video pose estimations without making use of motion capture data,” Kevin Xie explains in the paper.

“Our framework refines noisy pose estimates by enforcing physics constraints through contact invariant optimization, including computation of contact forces. We then train a time-series generative model on the refined poses, synthesizing both future motion and contact forces. Our results demonstrated significant performance boosts in both, pose estimation via our physics-based refinement, and motion synthesis results from video. We hope that our work will lead to more scalable human motion synthesis by leveraging large online video resources.”

Figure 2. AI captures the movement using motion capture to animate the individual into a digital avatar, and provide a physics simulation to accurately imitate the real-life movement.

This framework brings people one step closer to working and playing inside virtual worlds. It will help developers animate human motion far more affordably, with a much greater diversity of motions. From video games to the virtual world, this framework will undoubtedly impact how we visualize human motion synthesis.

Check out the framework or read the paper Physics-based Human Motion Estimation and Synthesis from Videos, by Kevin Xie, Tingwu Wang, Umar Iqbal, Yunrong Guo, Sanja Fidler, and Florian Shkurti.

Learn more about the NVIDIA Toronto AI lab.

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NVIDIA DLI Teaches Supervised and Unsupervised Anomaly Detection

Graphic with black background with DLI anomaly course nameLearn about multiple ML and DL techniques to detect anomalies in your organization’s data.Graphic with black background with DLI anomaly course name

The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to build applications of AI for anomaly detection. 

Anomaly detection is the process of identifying data that deviates abnormally within a data set. Different from the simpler process of identifying statistical outliers, anomaly detection seeks to discover data that should not be considered normal within its context. 

Anomalies can include data that are similar to captured and labeled anomalies, data that may be normal in a different context but not within the one it appeared, and data that can only be understood as anomalous through the insights of trained neural networks.

Anomaly detection is a powerful and important tool in many business and research contexts. Healthcare professionals use anomaly detection to identify signs of disease in humans earlier and more effectively. IT and DevOps teams for any number of businesses apply anomaly detection to identify events that may lead to performance degradation or loss of service. Teams in marketing and finance leverage anomaly detection to identify specific events with a large impact on their KPIs. 

In short, any team benefits from identifying the special cases in data relevant to their goals could potentially benefit from the effective use of anomaly detection.

Approaches to anomaly detection

It should come as no surprise that many approaches are available to perform anomaly detection, given its diverse range of important applications. One helpful factor in determining what approach will be most effective for a given scenario is whether labeled data already exist indicating which samples are anomalous. Supervised learning methods can be employed when an anomaly can be defined and sufficient representative data exists. Alternatively, unsupervised methods may be required in scenarios where no such labeled data is available and yet detection of novel anomalies is still necessary. 

The DLI workshop Applications of AI for Anomaly Detection cover both supervised and unsupervised cases. A supervised XGBoost model is employed to detect anomalous network traffic using the KDD network intrusion dataset. Additionally,  the model is trained to classify yet-unseen anomalous data not only as part of an attack, but also to identify the kind of attack.

Two approaches are considered for the unsupervised learning approach, beginning by training a deep autoencoder neural network. This is followed by introducing a two-network generative adversarial network (GAN), where the component discriminator network performs the anomaly detection. Below are more details on each of these approaches.

XGBoost details

XGBoost is an optimized gradient-boosting algorithm with a wide variety of applications. In addition to its extensive practical use cases, XGBoost has earned a strong reputation through its extensive and effective performance at Kaggle data science competitions. Given the presence of labeled data for training, the anomaly detection problem is considered a classification problem where a trained XGBoost model identifies anomalies in holdout test data. NVIDIA GPUs are leveraged to accelerate XGBoost by parallelizing training, first as a binary classifier, then as a multiclass classifier identifying the kind of anomaly.

AE details

Deep autoencoders consist of two symmetrical parts. The first part, called the encoder, compresses, or “encodes” data into a lower-dimensional latent representation. The second part, the decoder, attempts to reconstruct the original input from the latent vector produced by the encoder. During training, both encoder and decoder are optimized to create latent representations of the input data that better capture its essential aspects. When trained with a low prevalence of anomalies, the latent vector is better able to represent the plentiful samples of normal data than the anomalies. The output of the decoder will therefore more reliably reconstruct normal data than anomalies. Passing normal data through the autoencoder will generate relatively lower reconstruction errors than anomalies, and classification is accomplished by setting a threshold on this error.

GAN details

Generative adversarial networks consist of two neural networks that compete against each other to improve their overall performance. One network, the generator, learns to take a random seed and produce an artificial data sample drawn from the same distribution as training set data. The second network, the discriminator, learns to distinguish between samples from the training data set and those produced by the generator.

When trained properly, the generator learns to deliver realistic-looking artificial data samples while the discriminator can accurately identify data appearing like that from the training set. When trained with data representative of nonanomalous data, the generator is able to create new samples resembling normal data and the discriminator is able to classify samples as appearing normal.

Most typically, GANs are trained with the goal of using the generator to produce new, realistic-looking data samples while the discriminator is discarded. For anomaly detection, however, the generator is instead set aside and the discriminator leveraged to determine whether unknown input data is normal or anomalous. 

Learn more

AI-powered anomaly detection provides rich, and sometimes essential capabilities across a wide variety of fields. Furthermore, techniques applicable for anomaly detection can be used to great effect in other AI domains as well. 

If interested in anomaly detection, or extending your deep learning skills through hands-on interactive practice with expert instruction, sign up for an upcoming NVIDIA DLI workshop on Applications of AI for Anomaly Detection. This training is also available as a private workshop for organizations. 

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How to Read Research Papers: A Pragmatic Approach for ML Practitioners

This article presents an effective systematic method to approach reading research papers to be used as a resource for Machine Learning practitioners.

Is it necessary for data scientists or machine-learning experts to read research papers?

The short answer is yes. And don’t worry if you lack a formal academic background or have only obtained an undergraduate degree in the field of machine learning.

Reading academic research papers may be intimidating for individuals without an extensive educational background. However, a lack of academic reading experience should not prevent Data scientists from taking advantage of a valuable source of information and knowledge for machine learning and AI development.

This article provides a hands-on tutorial for data scientists of any skill level to read research papers published in academic journals such as NeurIPS, JMLR, ICML, and so on.

Before diving wholeheartedly into how to read research papers, the first phases of learning how to read research papers cover selecting relevant topics and research papers.

Step 1: Identify a topic

The domain of machine learning and data science is home to a plethora of subject areas that may be studied. But this does not necessarily imply that tackling each topic within machine learning is the best option.

Although generalization for entry-level practitioners is advised, I’m guessing that when it comes to long-term machine learning, career prospects, practitioners, and industry interest often shifts to specialization.

Identifying a niche topic to work on may be difficult, but good. Still, a rule of thumb is to select an ML field in which you are either interested in obtaining a professional position or already have experience.

Deep Learning is one of my interests, and I’m a Computer Vision Engineer that uses deep learning models in apps to solve computer vision problems professionally. As a result, I’m interested in topics like pose estimation, action classification, and gesture identification.

Based on roles, the following are examples of ML/DS occupations and related themes to consider.

Machine Learning and Data Science roles and associated topics.
Figure 1: Machine Learning and Data Science roles and associated topics. Image created by Author.

For this article, I’ll select the topic Pose Estimation to explore and choose associated research papers to study.

Step 2: Finding research papers

One of the most excellent tools to use while looking at machine learning-related research papers, datasets, code, and other related materials is PapersWithCode.

We use the search engine on the PapersWithCode website to get relevant research papers and content for our chosen topic, “Pose Estimation.” The following image shows you how it’s done.

Gif searching Pose Estimation
Figure 2: Image created by Author: GIF searching Pose Estimation.

The search results page contains a short explanation of the searched topic, followed by a table of associated datasets, models, papers, and code. Without going into too much detail, the area of interest for this use case is the “Greatest papers with code”. This section contains the relevant papers related to the task or topic. For the purpose of this article, I’ll select the DensePose: Dense Human Pose Estimation In The Wild.

Step 3: First pass (gaining context and understanding)

A notepad with a lightbulb drawn on it.
Figure 3: Gaining context and understanding. Photo by AbsolutVision on Unsplash

At this point, we’ve selected a research paper to study and are prepared to extract any valuable learnings and findings from its content.

It’s only natural that your first impulse is to start writing notes and reading the document from beginning to end, perhaps taking some rest in between. However, having a context for the content of a study paper is a more practical way to read it. The title, abstract, and conclusion are three key parts of any research paper to gain an understanding.

The goal of the first pass of your chosen paper is to achieve the following:

  • Assure that the paper is relevant.
  • Obtain a sense of the paper’s context by learning about its contents, methods, and findings.
  • Recognize the author’s goals, methodology, and accomplishments.

Title

The title is the first point of information sharing between the authors and the reader. Therefore, research papers titles are direct and composed in a manner that leaves no ambiguity.

The research paper title is the most telling aspect since it indicates the study’s relevance to your work. The importance of the title is to give a brief perception of the paper’s content.

In this situation, the title is “DensePose: Dense Human Pose Estimation in the Wild.” This gives a broad overview of the work and implies that it will look at how to provide pose estimations in environments with high levels of activity and realistic situations properly.

Abstract

The abstract portion gives a summarized version of the paper. It’s a short section that contains 300-500 words and tells you what the paper is about in a nutshell. The abstract is a brief text that provides an overview of the article’s content, researchers’ objectives, methods, and techniques.

When reading an abstract of a machine-learning research paper, you’ll typically come across mentions of datasets, methods, algorithms, and other terms. Keywords relevant to the article’s content provide context. It may be helpful to take notes and keep track of all keywords at this point.

For the paper: “DensePose: Dense Human Pose Estimation In The Wild“, I identified in the abstract the following keywords: pose estimation, COCO dataset, CNN, region-based models, real-time.

Conclusion

It’s not uncommon to experience fatigue when reading the paper from top to bottom at your first initial pass, especially for Data Scientists and practitioners with no prior advanced academic experience. Although extracting information from the later sections of a paper might seem tedious after a long study session, the conclusion sections are often short. Hence reading the conclusion section in the first pass is recommended.

The conclusion section is a brief compendium of the work’s author or authors and/or contributions and accomplishments and promises for future developments and limitations.

Before reading the main content of a research paper, read the conclusion section to see if the researcher’s contributions, problem domain, and outcomes match your needs.

Following this particular brief first pass step enables a sufficient understanding and overview of the research paper’s scope and objectives, as well as a context for its content. You’ll be able to get more detailed information out of its content by going through it again with laser attention.

Step 4: Second pass (content familiarization)

Content familiarization is a process that’s relevant to the initial steps. The systematic approach to reading the research paper presented in this article. The familiarity process is a step that involves the introduction section and figures within the research paper.

As previously mentioned, the urge to plunge straight into the core of the research paper is not required because knowledge acclimatization provides an easier and more comprehensive examination of the study in later passes.

Introduction

Introductory sections of research papers are written to provide an overview of the objective of the research efforts. This objective mentions and explains problem domains, research scope, prior research efforts, and methodologies.

It’s normal to find parallels to past research work in this area, using similar or distinct methods. Other papers’ citations provide the scope and breadth of the problem domain, which broadens the exploratory zone for the reader. Perhaps incorporating the procedure outlined in Step 3 is sufficient at this point.

Another aspect of the benefit provided by the introduction section is the presentation of requisite knowledge required to approach and understand the content of the research paper.

Graph, diagrams, figures

Illustrative materials within the research paper ensure that readers can comprehend factors that support problem definition or explanations of methods presented. Commonly, tables are used within research papers to provide information on the quantitative performances of novel techniques in comparison to similar approaches.

Image showing the Comparison of DensePose with other single person pose estimation solutions,
Figure 4: Image showing the Comparison of DensePose with other single person pose estimation solutions.

Generally, the visual representation of data and performance enables the development of an intuitive understanding of the paper’s context. In the Dense Pose paper mentioned earlier, illustrations are used to depict the performance of the author’s approach to pose estimation and create. An overall understanding of the steps involved in generating and annotating data samples.

In the realm of deep learning, it’s common to find topological illustrations depicting the structure of artificial neural networks. Again this adds to the creation of intuitive understanding for any reader. Through illustrations and figures, readers may interpret the information themselves and gain a fuller perspective of it without having any preconceived notions about what outcomes should be.

Image showing the cross-cascading architecture of DensePose.
Figure 5: Image showing the cross-cascading architecture of DensePose.

Step 5: Third pass (deep reading)

The third pass of the paper is similar to the second, though it covers a greater portion of the text. The most important thing about this pass is that you avoid any complex arithmetic or technique formulations that may be difficult for you. During this pass, you can also skip over any words and definitions that you don’t understand or aren’t familiar with. These unfamiliar terms, algorithms, or techniques should be noted to return to later.

Image of a magnifying glass depicting deep reading.
Figure 6: Deep reading. Photo by Markus Winkler on Unsplash.

During this pass, your primary objective is to gain a broad understanding of what’s covered in the paper. Approach the paper, starting again from the abstract to the conclusion, but be sure to take intermediary breaks in between sections. Moreover, it’s recommended to have a notepad, where all key insights and takeaways are noted, alongside the unfamiliar terms and concepts.

The Pomodoro Technique is an effective method of managing time allocated to deep reading or study. Explained simply, the Pomodoro Technique involves the segmentation of the day into blocks of work, followed by short breaks.

What works for me is the 50/15 split, that is, 50 minutes studying and 15 minutes allocated to breaks. I tend to execute this split twice consecutively before taking a more extended break of 30 minutes. If you are unfamiliar with this time management technique, adopt a relatively easy division such as 25/5 and adjust the time split according to your focus and time capacity.

Step 6: Forth pass (final pass)

The final pass is typically one that involves an exertion of your mental and learning abilities, as it involves going through the unfamiliar terms, terminologies, concepts, and algorithms noted in the previous pass. This pass focuses on using external material to understand the recorded unfamiliar aspects of the paper.

In-depth studies of unfamiliar subjects have no specified time length, and at times efforts span into the days and weeks. The critical factor to a successful final pass is locating the appropriate sources for further exploration.

 Unfortunately, there isn’t one source on the Internet that provides the wealth of information you require. Still, there are multiple sources that, when used in unison and appropriately, fill knowledge gaps. Below are a few of these resources.

The Reference sections of research papers mention techniques and algorithms. Consequently, the current paper either draws inspiration from or builds upon, which is why the reference section is a useful source to use in your deep reading sessions.

Step 7: Summary (optional)

In almost a decade of academic and professional undertakings of technology-associated subjects and roles, the most effective method of ensuring any new information learned is retained in my long-term memory through the recapitulation of explored topics. By rewriting new information in my own words, either written or typed, I’m able to reinforce the presented ideas in an understandable and memorable manner.

An image of someone blogging on a laptop
Figure 7: Blogging and summarizing. Photo by NeONBRAND on Unsplash

To take it one step further, it’s possible to publicize learning efforts and notes through the utilization of blogging platforms and social media. An attempt to explain the freshly explored concept to a broad audience, assuming a reader isn’t accustomed to the topic or subject, requires understanding topics in intrinsic details.

Conclusion

Undoubtedly, reading research papers for novice Data Scientists and ML practitioners can be daunting and challenging; even seasoned practitioners find it difficult to digest the content of research papers in a single pass successfully.

The nature of the Data Science profession is very practical and involved. Meaning, there’s a requirement for its practitioners to employ an academic mindset, more so as the Data Science domain is closely associated with AI, which is still a developing field.

To summarize, here are all of the steps you should follow to read a research paper:

  • Identify A Topic.
  • Finding associated Research Papers
  • Read title, abstract, and conclusion to gain a vague understanding of the research effort aims and achievements.
  • Familiarize yourself with the content by diving deeper into the introduction; including the exploration of figures and graphs presented in the paper.
  • Use a deep reading session to digest the main content of the paper as you go through the paper from top to bottom.
  • Explore unfamiliar terms, terminologies, concepts, and methods using external resources.
  • Summarize in your own words essential takeaways, definitions, and algorithms.

Thanks for reading!

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Training Machine Learning Models More Efficiently with Dataset Distillation

Posted by Timothy Nguyen1, Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research

For a machine learning (ML) algorithm to be effective, useful features must be extracted from (often) large amounts of training data. However, this process can be made challenging due to the costs associated with training on such large datasets, both in terms of compute requirements and wall clock time. The idea of distillation plays an important role in these situations by reducing the resources required for the model to be effective. The most widely known form of distillation is model distillation (a.k.a. knowledge distillation), where the predictions of large, complex teacher models are distilled into smaller models.

An alternative option to this model-space approach is dataset distillation [1, 2], in which a large dataset is distilled into a synthetic, smaller dataset. Training a model with such a distilled dataset can reduce the required memory and compute. For example, instead of using all 50,000 images and labels of the CIFAR-10 dataset, one could use a distilled dataset consisting of only 10 synthesized data points (1 image per class) to train an ML model that can still achieve good performance on the unseen test set.

Top: Natural (i.e., unmodified) CIFAR-10 images. Bottom: Distilled dataset (1 image per class) on CIFAR-10 classification task. Using only these 10 synthetic images as training data, a model can achieve test set accuracy of ~51%.

In “Dataset Meta-Learning from Kernel Ridge Regression”, published in ICLR 2021, and “Dataset Distillation with Infinitely Wide Convolutional Networks”, presented at NeurIPS 2021, we introduce two novel dataset distillation algorithms, Kernel Inducing Points (KIP) and Label Solve (LS), which optimize datasets using the loss function arising from kernel regression (a classical machine learning algorithm that fits a linear model to features defined through a kernel). Applying the KIP and LS algorithms, we obtain very efficient distilled datasets for image classification, reducing the datasets to 1, 10, or 50 data points per class while still obtaining state-of-the-art results on a number of benchmark image classification datasets. Additionally, we are also excited to release our distilled datasets to benefit the wider research community.

Methodology
One of the key theoretical insights of deep neural networks (DNN) in recent years has been that increasing the width of DNNs results in more regular behavior that makes them easier to understand. As the width is taken to infinity, DNNs trained by gradient descent converge to the familiar and simpler class of models arising from kernel regression with respect to the neural tangent kernel (NTK), a kernel that measures input similarity by computing dot products of gradients of the neural network. Thanks to the Neural Tangents library, neural kernels for various DNN architectures can be computed in a scalable manner.

We utilized the above infinite-width limit theory of neural networks to tackle dataset distillation. Dataset distillation can be formulated as a two-stage optimization process: an “inner loop” that trains a model on learned data, and an “outer loop” that optimizes the learned data for performance on natural (i.e., unmodified) data. The infinite-width limit replaces the inner loop of training a finite-width neural network with a simple kernel regression. With the addition of a regularizing term, the kernel regression becomes a kernel ridge-regression (KRR) problem. This is a highly valuable outcome because the kernel ridge regressor (i.e., the predictor from the algorithm) has an explicit formula in terms of its training data (unlike a neural network predictor), which means that one can easily optimize the KRR loss function during the outer loop.

The original data labels can be represented by one-hot vectors, i.e., the true label is given a value of 1 and all other labels are given values of 0. Thus, an image of a cat would have the label “cat” assigned a 1 value, while the labels for “dog” and “horse” would be 0. The labels we use involve a subsequent mean-centering step, where we subtract the reciprocal of the number of classes from each component (so 0.1 for 10-way classification) so that the expected value of each label component across the dataset is normalized to zero.

While the labels for natural images appear in this standard form, the labels for our learned distilled datasets are free to be optimized for performance. Having obtained the kernel ridge regressor from the inner loop, the KRR loss function in the outer loop computes the mean-square error between the original labels of natural images and the labels predicted by the kernel ridge regressor. KIP optimizes the support data (images and possibly labels) by minimizing the KRR loss function through gradient-based methods. The Label Solve algorithm directly solves for the set of support labels that minimizes the KRR loss function, generating a unique dense label vector for each (natural) support image.

Example of labels obtained by label solving. Left and Middle: Sample images with possible labels listed below. The raw, one-hot label is shown in blue and the final LS generated dense label is shown in orange. Right: The covariance matrix between original labels and learned labels. Here, 500 labels were distilled from the CIFAR-10 dataset. A test accuracy of 69.7% is achieved using these labels for kernel ridge-regression.

Distributed Computation
For simplicity, we focus on architectures that consist of convolutional neural networks with pooling layers. Specifically, we focus on the so-called “ConvNet” architecture and its variants because it has been featured in other dataset distillation studies. We used a slightly modified version of ConvNet that has a simple architecture given by three blocks of convolution, ReLu, and 2×2 average pooling and then a final linear readout layer, with an additional 3×3 convolution and ReLu layer prepended (see our GitHub for precise details).

ConvNet architecture used in DC/DSA. Ours has an additional 3×3 Conv and ReLu prepended.

To compute the neural kernels needed in our work, we used the Neural Tangents library.

The first stage of this work, in which we applied KRR, focused on fully-connected networks, whose kernel elements are cheap to compute. But a hurdle facing neural kernels for models with convolutional layers plus pooling is that the computation of each kernel element between two images scales as the square of the number of input pixels (due to the capturing of pixel-pixel correlations by the kernel). So, for the second stage of this work, we needed to distribute the computation of the kernel elements and their gradients across many devices.

Distributed computation for large scale metalearning.

We invoke a client-server model of distributed computation in which a server distributes independent workloads to a large pool of client workers. A key part of this is to divide the backpropagation step in a way that is computationally efficient (explained in detail in the paper).

We accomplish this using the open-source tools Courier (part of DeepMind’s Launchpad), which allows us to distribute computations across GPUs working in parallel, and JAX, for which novel usage of the jax.vjp function enables computationally efficient gradients. This distributed framework allows us to utilize hundreds of GPUs per distillation of the dataset, for both the KIP and LS algorithms. Given the compute required for such experiments, we are releasing our distilled datasets to benefit the wider research community.

Examples
Our first set of distilled images above used KIP to distill CIFAR-10 down to 1 image per class while keeping the labels fixed. Next, in the below figure, we compare the test accuracy of training on natural MNIST images, KIP distilled images with labels fixed, and KIP distilled images with labels optimized. We highlight that learning the labels provides an effective, albeit mysterious benefit to distilling datasets. Indeed the resulting set of images provides the best test performance (for infinite-width networks) despite being less interpretable.

MNIST dataset distillation with trainable and non-trainable labels. Top: Natural MNIST data. Middle: Kernel Inducing Point distilled data with fixed labels. Bottom: Kernel Inducing Point distilled data with learned labels.

Results
Our distilled datasets achieve state-of-the-art performance on benchmark image classification datasets, improving performance beyond previous state-of-the-art models that used convolutional architectures, Dataset Condensation (DC) and Dataset Condensation with Differentiable Siamese Augmentation (DSA). In particular, for CIFAR-10 classification tasks, a model trained on a dataset consisting of only 10 distilled data entries (1 image / class, 0.02% of the whole dataset) achieves a 64% test set accuracy. Here, learning labels and an additional image preprocessing step leads to a significant increase in performance beyond the 50% test accuracy shown in our first figure (see our paper for details). With 500 images (50 images / class, 1% of the whole dataset), the model reaches 80% test set accuracy. While these numbers are with respect to neural kernels (using the KRR infinite width limit), these distilled datasets can be used to train finite-width neural networks as well. In particular, for 10 data points on CIFAR-10, a finite-width ConvNet neural network achieves 50% test accuracy with 10 images and 68% test accuracy using 500 images, which are still state-of-the-art results. We provide a simple Colab notebook demonstrating this transfer to a finite-width neural network.

Dataset distillation using Kernel Inducing Points (KIP) with a convolutional architecture outperforms prior state-of-the-art models (DC/DSA) on all benchmark settings on image classification tasks. Label Solve (LS, middle columns) while only distilling information in the labels could often (e.g. CIFAR-10 10, 50 data points per class) outperform prior state-of-the-art models as well.

In some cases, our learned datasets are more effective than a natural dataset one hundred times larger in size.

Conclusion
We believe that our work on dataset distillation opens up many interesting future directions. For instance, our algorithms KIP and LS have demonstrated the effectiveness of using learned labels, an area that remains relatively underexplored. Furthermore, we expect that utilizing efficient kernel approximation methods can help to reduce computational burden and scale up to larger datasets. We hope this work encourages researchers to explore other applications of dataset distillation, including neural architecture search and continual learning, and even potential applications to privacy.

Anyone interested in the KIP and LS learned datasets for further analysis is encouraged to check out our papers [ICLR 2021, NeurIPS 2021] and open-sourced code and datasets available on Github.

Acknowledgement
This project was done in collaboration with Zhourong Chen, Roman Novak and Lechao Xiao. We would like to acknowledge special thanks to Samuel S. Schoenholz, who proposed and helped develop the overall strategy for our distributed KIP learning methodology.


1Now at DeepMind.  ↩