Mehryar Mohri

Ameet Talwalkar

Afshin Rostamizadeh

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

Foundations of Machine Learning

Ben Hutchinson

Qiang Yang

Graham Cormode

Yang Liu

David Evans

Han Yu

Ramesh Raskar

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

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

Mehryar Mohri

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