02655nam 22005055 450 99664797020331620250303115226.09783031822407(electronic bk.)978303182239110.1007/978-3-031-82240-7(MiAaPQ)EBC31942155(Au-PeEL)EBL31942155(CKB)37772225600041(DE-He213)978-3-031-82240-7(EXLCZ)993777222560004120250303d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierFederated 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 /edited by Han Yu, Xiaoxiao Li, Zenglin Xu, Randy Goebel, Irwin King1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (295 pages)Lecture Notes in Artificial Intelligence,2945-9141 ;15501Print version: Yu, Han Federated Learning in the Age of Foundation Models - FL 2024 International Workshops Cham : Springer,c2025 9783031822391 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. .Lecture Notes in Artificial Intelligence,2945-9141 ;15501Artificial intelligenceArtificial IntelligenceArtificial intelligence.Artificial Intelligence.006.3Yu Han1063443Li Xiaoxiao1783536Xu Zenglin1789993Goebel Randy305376King Irwin1739354MiAaPQMiAaPQMiAaPQ996647970203316Federated Learning in the Age of Foundation Models - FL 2024 International Workshops4326147UNISA