Computing and Information Technology, Computer Science, Artificial Intelligence (ai)

Federated Learning in the Age of Foundation Models: FL 2024 International Workshops : FL@FM-WWW 2024, Singapore, May 14, 2024, FL@FM-ICME 2024, Niagara Falls, ON, Canada, July 15, 2024, FL@FM-IJCAI 2024, Jeju Island, South Korea, August 5, 2024, and FL@FM-NeurIPS 2024, Vancouver, BC, Canada, December 15, 2024, Revised Selected Papers

Han Yu (editor-in-chief), Xiaoxiao Li (editor-in-chief), Zenglin Xu (editor-in-chief), Randy Goebel (editor-in-chief), Irwin King (editor-in-chief), FL@FM-WWW (other), FL@FM-ICME (other), FL@FM-IJCAI (other), FL@FM-NeurIPS (other)

Paperback Published on: 04/03/2025

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Synopsis

This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:

FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics:

Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization.

Publisher information

Publisher: Springer Nature Switzerland
ISBN: 9783031822391
Number of pages: 182
Dimensions: 154 x 235 x 14 mm
Weight: 314g
Languages: English