Alex Ingerman
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Privacy-first Health Research with Federated Learning
Elaine O. Nsoesie
Dung Nguyen
Methun Kamruzzaman
Jamie MacFarlane
Benjamin Rader
John S. Brownstein
Madhav Marathe
Anil Vullikanti
Adam Sadilek
Paul C. Eastham
Stefan Mellem
npj Digital Medicine (2021)
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Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show—on a diverse set of single and multi-site health studies—that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research—across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science—aspects that used to be at odds with each other.
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A Field Guide to Federated Optimization
Suhas Diggavi
Chaoyang He
Mahdi Soltanolkotabi
Maruan Al-Shedivat
Chen Zhu
Peter Richtarik
Honglin Yuan
Ameet Talwalkar
Sebastian Stich
Sanmi Koyejo
Hongyi Wang
Deepesh Data
Blake Woodworth
Filip Hanzely
A. Salman Avestimehr
Tian Li
Jianyu Wang
Samuel Horvath
Antonious M. Girgis
Mi Zhang
Advait Gadhikar
Martin Jaggi
Gauri Joshi
Tara Javidi
Virginia Smith
Sai Praneeth Karimireddy
Karan Singhal
Jakub Konečný
Satyen Chandrakant Kale
Chunxiang (Jake) Zheng
Weikang Song
Galen Andrew
Katharine Daly
Tong Zhang
Hubert Eichner
arxiv (2021)
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Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.
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Towards Federated Learning at Scale: System Design
David Petrou
Jakub Konečný
Wolfgang Grieskamp
Stefano Mazzocchi
Dzmitry Huba
Vladimir Ivanov
Timon Van Overveldt
Jason Roselander
Chloé M Kiddon
Hubert Eichner
SysML 2019
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Federated Learning is a distributed machine learning approach which enables training on a large corpus of data which never needs to leave user devices. We have spent some effort over the last two years building a scalable production system for FL. In this paper, we report about the resulting high-level design, sketching the challenges and the solutions, as well as touching the open problems and future directions.
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