Foundations and Trends® in Machine Learning > Vol 14 > Issue 1

Advances and Open Problems in Federated Learning

Edited by: Peter Kairouz, Google Research, USA, Kairouz@google.com H. Brendan McMahan, Google Research, USA,
 
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Edited by: Peter Kairouz and H. Brendan McMahan (2021), "Advances and Open Problems in Federated Learning", Foundations and Trends® in Machine Learning: Vol. 14: No. 1. http://dx.doi.org/10.1561/2200000083

Forthcoming: 31 Mar 2021
© 2021 Peter Kairouz, H. Brendan McMahan and contributors
 
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In this article:
1. Introduction 
2. Relaxing the Core FL Assumptions: Applications to Emerging Settings and Scenarios 
3. Improving Efficiency and Effectiveness 
4. Preserving the Privacy of User Data 
5. Robustness to Attacks and Failures 
6. Ensuring Fairness and Addressing Sources of Bias 
7. Concluding Remarks 
A. Software and Datasets for Federated Learning 
References 

Abstract

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

DOI:10.1561/2200000083
ISBN: 978-1-68083-788-9
114 pp. $99.00
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ISBN: 978-1-68083-789-6
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Table of contents:
1. Introduction
2. Relaxing the Core FL Assumptions: Applications to Emerging Settings and Scenarios
3. Improving Efficiency and Effectiveness
4. Preserving the Privacy of User Data
5. Robustness to Attacks and Failures
6. Ensuring Fairness and Addressing Sources of Bias
7. Concluding Remarks
A. Software and Datasets for Federated Learning
References

Advances and Open Problems in Federated Learning

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.

Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.

This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.

Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

 
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