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This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.ĭeep neural networks are susceptible to various inference attacks as they remember information about their training data. 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.

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. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. 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.

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. 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.

Apple serial number finally solved analysis