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.
 

Dettagli sul prodotto

• Con la collaborazione di: Randy Goebel (Editore), Irwin King (Editore), Xiaoxiao Li (Editore), Zeng Lin Xu et al (Editore), Zeng Lin Xu (Editore), Zenglin Xu (Editore), Zenglin Xu et al (Editore), Han Yu (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 18.02.2025
• EAN: 9783031822391
• ISBN: 978-3-0-3182239-1
• Pagine: 182
• Dimensioni: 155 mm x 11 mm x 235 mm
• Peso: 306 g
• Illustrazioni: XII, 182 p. 52 illus., 50 illus. in color.
• Serie: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica; Artificial Intelligence, personalization, Federated Learning, foundation models, Privacy Preservation, Large-Language Models