株式会社極東書店トップ ＞ 商品一覧 ＞ 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.
商品詳細
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.
・ISBN 978-3-031-82239-1 paper EUR 49.99
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| 著者・編者 | Yu, Han / Li, Xiaoxiao / Xu, Zenglin / Goebel, Randy / King, Irwin (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2025 |
| ページ数 | 182 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A03-85463＞ |
解説
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.