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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| Kolejni autorzy: | , , |
| Format: | Elektroniczne E-book |
| Język: | angielski |
| Wydane: |
Cham :
Springer International Publishing : Imprint: Springer,
2025.
|
| Wydanie: | 1st ed. 2025. |
| Seria: | Springer Optimization and Its Applications,
213 |
| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1007/978-3-031-58923-2 |
| Etykiety: |
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| 245 | 1 | 0 | |a Handbook of Trustworthy Federated Learning |h [electronic resource] / |c edited by My T. Thai, Hai N. Phan, Bhavani Thuraisingham. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (X, 428 p. 104 illus.). |b 94 illus. in color. | ||
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| 490 | 1 | |a Springer Optimization and Its Applications, |x 1931-6836 ; |v 213 | |
| 505 | 0 | |a -- 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 -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. | |
| 520 | |a 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 topics. The readership spans from researchers and academics to practitioners who are deeply engaged or are starting to venture into the realms of Trustworthy Federated Learning. First introduced in 2016, federated learning allows devices to collaboratively learn a shared model while keeping raw data localized, thus promising to protect data privacy. Since its introduction, federated learning has undergone several evolutions. Most importantly, its evolution is in response to the growing recognition that its promise of collaborative learning is inseparable from the imperatives of privacy preservation and model security. The resource is divided into four parts. Part 1 (Security and Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. Part 2 (Bilevel Optimization) unravels the intricacies of optimizing performance in federated settings. Part 3 (Graph and Large Language Models) addresses the challenges in training Graph Neural Networks and ensuring privacy in Federated Learning of natural language models. Part 4 (Edge Intelligence and Applications) demonstrates how Federated Learning can empower mobile applications and preserve privacy with synthetic data. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Data protection. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Machine learning. | |
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| 700 | 1 | |a Phan, Hai N. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Thuraisingham, Bhavani. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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