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Trustworthy Federated Learning

First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Randy Goebel

    1. University of Alberta, Edmonton, Canada

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  2. Han Yu

    1. Nanyang Technological University, Singapore, Singapore

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  3. Boi Faltings

    1. École polytechnique fédérale de Lausanne, Lausanne, Switzerland

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  4. Lixin Fan

    1. WeBank, Shenzhen, China

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  5. Zehui Xiong

    1. Singapore University of Technology and Design, Singapore, Singapore

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13448)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: FL 2022.

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Table of contents (11 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Adaptive Expert Models for Federated Learning

    • Martin Isaksson, Edvin Listo Zec, Rickard Cöster, Daniel Gillblad, Sarunas Girdzijauskas
    Pages 1-16

  3. Federated Learning with GAN-Based Data Synthesis for Non-IID Clients

    • Zijian Li, Jiawei Shao, Yuyi Mao, Jessie Hui Wang, Jun Zhang
    Pages 17-32

  4. Practical and Secure Federated Recommendation with Personalized Mask

    • Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen et al.
    Pages 33-45

  5. A General Theory for Client Sampling in Federated Learning

    • Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
    Pages 46-58

  6. Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration

    • Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren, Sarunas Girdzijauskas
    Pages 59-71

  7. Sketch to Skip and Select: Communication Efficient Federated Learning Using Locality Sensitive Hashing

    • Georgios Kollias, Theodoros Salonidis, Shiqiang Wang
    Pages 72-83

  8. Fast Server Learning Rate Tuning for Coded Federated Dropout

    • Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic, Gerald Q. Maguire Jr.
    Pages 84-99

  9. FedAUXfdp: Differentially Private One-Shot Federated Distillation

    • Haley Hoech, Roman Rischke, Karsten Müller, Wojciech Samek
    Pages 100-114

  10. Secure Forward Aggregation for Vertical Federated Neural Networks

    • Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang et al.
    Pages 115-129

  11. Two-Phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting

    • Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu, Zengxiang Li
    Pages 130-143

  12. Privacy-Preserving Federated Cross-Domain Social Recommendation

    • Jianping Cai, Yang Liu, Ximeng Liu, Jiayin Li, Hongbin Zhuang
    Pages 144-158

  13. Back Matter

    Pages 159-159
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  1. Trustworthy Federated Learning

Keywords

About this book

This book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. 
The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.


Editors and Affiliations

  • University of Alberta, Edmonton, Canada

    Randy Goebel

  • Nanyang Technological University, Singapore, Singapore

    Han Yu

  • École polytechnique fédérale de Lausanne, Lausanne, Switzerland

    Boi Faltings

  • WeBank, Shenzhen, China

    Lixin Fan

  • Singapore University of Technology and Design, Singapore, Singapore

    Zehui Xiong

Bibliographic Information

  • Book Title: Trustworthy Federated Learning

  • Book Subtitle: First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers

  • Editors: Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-28996-5

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Softcover ISBN: 978-3-031-28995-8Published: 29 March 2023

  • eBook ISBN: 978-3-031-28996-5Published: 28 March 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 159

  • Number of Illustrations: 4 b/w illustrations, 49 illustrations in colour

  • Topics: Artificial Intelligence, Systems and Data Security, Computer Applications

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