Handbook of Trustworthy Federated Learning
This Handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on Federated Learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various to...
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| 団体著者: | |
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| その他の著者: | , , |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Cham :
Springer International Publishing : Imprint: Springer,
2025.
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| 版: | 1st ed. 2025. |
| シリーズ: | Springer Optimization and Its Applications,
213 |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-3-031-58923-2 |
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目次:
- -- Trustworthiness, Privacy and Security in Federated Learning. - Secure Federated Learning
- Data Poisoning and Leakage Analysis in Federated Learning
- Robust Federated Learning against Targeted Attackers using Model Updates Correlation
- Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness
- Federated Bilevel Optimization
- A Two-Stage Stochastic Programming Approach for the Key Management 𝒒-Composite Scheme
- Recent Advances in Federated Graph Learning
- Privacy in Federated Learning Natural Language Models
- Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
- Robust Federated Learning for Edge Intelligence
- ZoneFL: Zone-based Federated Learning at the Edge
- Synthetic Data for Privacy Preservation in Distributed Data Analysis Systems
- Towards Green Federated Learning.