Qiang Yang

Matthew E. Taylor

Roozbeh Razavi-Far

Boyu Wang

This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.

Federated and Transfer Learning

Karl Tuyls

This volume constitutes the thoroughly refereed post-conference proceedings of the Second Workshop on Adaptive and Learning Agents, ALA 2009, held as part of the AAMAS 2009 conference in Budapest, Hungary, in May 2009. The 8 revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a variety of themes: single and multi-agent reinforcement learning, the evolution and emergence of cooperation in agent systems, sensor networks and coordination in multi-resource job scheduling.

Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers

Adaptive Learning Agents

Yang Gao

Edith Elkind

Yang Yu

This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. Due to the Corona pandemic this event was held virtually.

Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings

Distributed Artificial Intelligence

Matthew E. Taylor

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