Alex Ingerman
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A Field Guide to Federated Optimization
Jianyu Wang
Zheng Xu
Gauri Joshi
Maruan Al-Shedivat
Galen Andrew
A. Salman Avestimehr
Katharine Daly
Deepesh Data
Suhas Diggavi
Hubert Eichner
Advait Gadhikar
Antonious M. Girgis
Filip Hanzely
Chaoyang He
Samuel Horvath
Martin Jaggi
Tara Javidi
Satyen Chandrakant Kale
Sai Praneeth Karimireddy
Jakub Konečný
Sanmi Koyejo
Tian Li
Luyang Liu
Peter Richtarik
Karan Singhal
Virginia Smith
Mahdi Soltanolkotabi
Weikang Song
Sebastian Stich
Ameet Talwalkar
Hongyi Wang
Blake Woodworth
Honglin Yuan
Manzil Zaheer
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
Wennan Zhu
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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Privacy-first Health Research with Federated Learning
Adam Sadilek
Luyang Liu
Dung Nguyen
Methun Kamruzzaman
Benjamin Rader
Stefan Mellem
Elaine O. Nsoesie
Jamie MacFarlane
Anil Vullikanti
Madhav Marathe
Paul C. Eastham
John S. Brownstein
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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Towards Federated Learning at Scale: System Design
K. A. Bonawitz
Hubert Eichner
Wolfgang Grieskamp
Dzmitry Huba
Vladimir Ivanov
Chloé M Kiddon
Jakub Konečný
Stefano Mazzocchi
Timon Van Overveldt
David Petrou
Jason Roselander
SysML 2019
Preview abstract
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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