A Field Guide to Federated Optimization
Abstract
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.
Cite
@article{arxiv.2107.06917,
title = {A Field Guide to Federated Optimization},
author = {Jianyu Wang and Zachary Charles and Zheng Xu and Gauri Joshi and H. Brendan McMahan and Blaise Aguera y Arcas and Maruan Al-Shedivat and Galen Andrew and Salman Avestimehr and Katharine Daly and Deepesh Data and Suhas Diggavi and Hubert Eichner and Advait Gadhikar and Zachary Garrett and Antonious M. Girgis and Filip Hanzely and Andrew Hard and Chaoyang He and Samuel Horvath and Zhouyuan Huo and Alex Ingerman and Martin Jaggi and Tara Javidi and Peter Kairouz and Satyen Kale and Sai Praneeth Karimireddy and Jakub Konecny and Sanmi Koyejo and Tian Li and Luyang Liu and Mehryar Mohri and Hang Qi and Sashank J. Reddi and Peter Richtarik and Karan Singhal and Virginia Smith and Mahdi Soltanolkotabi and Weikang Song and Ananda Theertha Suresh and Sebastian U. Stich and Ameet Talwalkar and Hongyi Wang and Blake Woodworth and Shanshan Wu and Felix X. Yu and Honglin Yuan and Manzil Zaheer and Mi Zhang and Tong Zhang and Chunxiang Zheng and Chen Zhu and Wennan Zhu},
journal= {arXiv preprint arXiv:2107.06917},
year = {2021}
}