LEADER 02655nam  22005055  450 
001 996647970203316
005 20250303115226.0
010   $a9783031822407$b(electronic bk.)
010   $z9783031822391
024 7 $a10.1007/978-3-031-82240-7
035   $a(MiAaPQ)EBC31942155
035   $a(Au-PeEL)EBL31942155
035   $a(CKB)37772225600041
035   $a(DE-He213)978-3-031-82240-7
035   $a(EXLCZ)9937772225600041
100   $a20250303d2025      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFederated Learning in the Age of Foundation Models - FL 2024 International Workshops $eFL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024, Niagara Falls, ON, Canada, July 15, 2024; FL@FM-IJCAI 2024, Jeju Island, South Korea, August 5, 2024; and FL@FM-NeurIPS 2024, Vancouver, BC, Canada, December 15, 2024, Revised Selected Papers /$fedited by Han Yu, Xiaoxiao Li, Zenglin Xu, Randy Goebel, Irwin King
205   $a1st ed. 2025.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025.
215   $a1 online resource (295 pages)
225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15501
311 08$aPrint version: Yu, Han Federated Learning in the Age of Foundation Models - FL 2024 International Workshops Cham : Springer,c2025 9783031822391 
330   $aThis LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows: FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics: Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization. .
410  0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15501
606   $aArtificial intelligence
606   $aArtificial Intelligence
615  0$aArtificial intelligence.
615 14$aArtificial Intelligence.
676   $a006.3
700   $aYu$b Han$01063443
701   $aLi$b Xiaoxiao$01783536
701   $aXu$b Zenglin$01789993
701   $aGoebel$b Randy$0305376
701   $aKing$b Irwin$01739354
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a996647970203316
996   $aFederated Learning in the Age of Foundation Models - FL 2024 International Workshops$94326147
997   $aUNISA