Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Handbook of Trustworthy Federated Learning

Handbook of Trustworthy Federated Learning

Thai, My T.
Phan, Hai N.
Thuraisingham, Bhavani

Hardcover

Series: Springer Optimization and Its Applications, Book 213

General MathematicsProbability & StatisticsComputer Security

ISBN10: 303158922X
ISBN13: 9783031589225
Publisher: Springer
Published: Sep 4 2024
Pages: 428
Weight: 1.74
Height: 1.00 Width: 6.14 Depth: 9.21
Language: English

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.

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