Ben Hutchinson

Qiang Yang

Graham Cormode

Yang Liu

David Evans

Han Yu

Ramesh Raskar

Mehryar Mohri

Zheng Xu

Rachel Cummings

Dawn Song

Mehdi Bennis

Peter Kairouz

H. Brendan McMahan

Brendan Avent

Aurélien Bellet

Arjun Nitin Bhagoji

Kallista Bonawit

Zachary Charles

Rafael G. L. D'Oliveira

Hubert Eichner

Salim El Rouayheb

Josh Gardner

Zachary Garrett

Adrià Gascón

Badih Ghazi

Phillip B. Gibbons

Marco Gruteser

Zaid Harchaoui

Chaoyang He

Lie He

Zhouyuan Huo

Justin Hsu

Martin Jaggi

Tara Javidi

Gauri Joshi

Mikhail Khodak

Jakub Konecný

Aleksandra Korolova

Farinaz Koushanfar

Sanmi Koyejo

Tancrède Lepoint

Prateek Mittal

Richard Nock

Ayfer Özgür

Rasmus Pagh

Daniel Ramage

Mariana Raykova

Weikang Song

Sebastian U. Stich

Ziteng Sun

Ananda Theertha Suresh

Florian Tramèr

Praneeth Vepakomma

Jianyu Wang

Li Xiong

Felix X. Yu

Sen Zhao

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.

Advances and Open Problems in Federated Learning

Books like Advances and Open Problems in Federated Learning

Your next favorite book is out there