Tara Javidi

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.

Wireless multi-hop networks have become an important part of many modern communication systems. Some of the earliest examples were military communication networks utilizing wireless relays in remote areas. More recently, many industries have used wireless multi-hop networks to create a multitude of fascinating tools and systems. Motivated by classical routing solutions in the Internet, conventional routing attempts to find a fixed path along which the packets are forwarded. Such fixed path schemes fail to take advantage of the broadcast nature and opportunities provided by the wireless medium, and result in unnecessary packet retransmissions. In opportunistic routing, decisions are made in an online manner by choosing the next relay based on the actual transmission outcomes as well as a rank ordering of neighboring nodes. Opportunistic Routing in Wireless Networks provides the motivation for opportunistic routing, and presents several different algorithms which achieve better performance, in most scenarios, than conventional shortest path routing. This comprehensive survey provides a starting point for all researchers and students wishing to learn the background and technical details of opportunistic routing.