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

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.
 

Product details

• Assisted by: Randy Goebel (Editor), Irwin King (Editor), Xiaoxiao Li (Editor), Zeng Lin Xu et al (Editor), Zeng Lin Xu (Editor), Zenglin Xu (Editor), Zenglin Xu et al (Editor), Han Yu (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 18.02.2025
• EAN: 9783031822391
• ISBN: 978-3-0-3182239-1
• No. of pages: 182
• Illustrations: XII, 182 p. 52 illus., 50 illus. in color.
• Series: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Artificial Intelligence, personalization, Federated Learning, foundation models, Privacy Preservation, Large-Language Models