Federated and Transfer Learning

Inbunden, Engelska, 2022

Av Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang

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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.

Produktinformation

Utgivningsdatum2022-10-01
Mått155 x 235 x 26 mm
Vikt735 g
FormatInbunden
SpråkEngelska
SerieAdaptation, Learning, and Optimization
Antal sidor371
FörlagSpringer International Publishing AG
ISBN9783031117473

Tillhör följande kategorier

Artificiell intelligens inom Data och it
Systemvetenskap och AI inom Data och it

An Introduction to Federated and Transfer Learning.- Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art.- Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms.- Cross-silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems.- A Unifying Framework for Federated Learning.- A Contract Theory based Incentive Mechanism for Federated Learning.- A Study of Blockchain-based Federated Learning.- Swarm Meta Learning.- Rethinking Importance Weighting for Transfer Learning.- Transfer Learning via Representation Learning.- Modeling Individual Humans via a Secondary Task Transfer Learning Method.- From Theoretical to Practical Transfer Learning: The Adapt Library.- Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning.- A Study on Efficient Reinforcement Learning Through Knowledge Transfer.- Federated Transfer Reinforcement Learning for Autonomous Driving.