Federated and Transfer Learning
| Corporate Author: | |
|---|---|
| Other Authors: | , , , |
| Summary: | VIII, 371 p. 90 illus., 80 illus. in color. text |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Edition: | 1st ed. 2023. |
| Series: | Adaptation, Learning, and Optimization,
27 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-031-11748-0 |
| Format: | Electronic Book |
Table of Contents:
- 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.