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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 / Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong, editors.

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 i...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Authors: International Workshop on Trustworthy Federated Learning Vienna, Austria, International Joint Conference on Artificial Intelligence
Other Authors: Goebel, Randy, Yu, Han (Assistant Professor), Faltings, Boi, Fan, Lixin (Scientist), Xiong, Zehui
Format: Conference Proceeding eBook
Language:English
Published: Cham : Springer, 2023.
Series:Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 13448.
Subjects:
Machine learning > Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Adaptive Expert Models for Federated Learning
  • 1 Introduction
  • 2 Background
  • 2.1 Problem Formulation
  • 2.2 Regimes of Non-IID Data
  • 2.3 Federated Learning
  • 2.4 Iterative Federated Clustering
  • 2.5 Federated Learning Using a Mixture of Experts
  • 3 Adaptive Expert Models for Personalization
  • 3.1 Framework Overview and Motivation
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Non-IID Sampling
  • 4.3 Model Architecture
  • 4.4 Hyperparameter Tuning
  • 4.5 Results
  • 5 Related Work
  • 6 Discussion
  • 7 Conclusion
  • References.
  • Federated Learning with GAN-Based Data Synthesis for Non-IID Clients
  • 1 Instruction
  • 2 Related Works
  • 3 Preliminary
  • 4 Synthetic Data Aided Federated Learning (SDA-FL)
  • 5 Experiments
  • 5.1 Experiment Setup
  • 5.2 Evaluation Results
  • 6 Conclusions and Discussions
  • References
  • Practical and Secure Federated Recommendation with Personalized Mask
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Matrix Factorization
  • 2.2 Federated Matrix Factorization
  • 3 Federated Masked Matrix Factorization
  • 3.1 Personalized Mask
  • 3.2 Adaptive Secure Aggregation
  • 4 Experiments
  • 4.1 Settings.
  • 4.2 Efficiency Promotion and Privacy Discussion
  • 4.3 Discussion on Model Effectiveness
  • 5 Conclusion
  • References
  • A General Theory for Client Sampling in Federated Learning
  • 1 Introduction
  • 2 Background
  • 2.1 Aggregating Clients Local Updates
  • 2.2 Unbiased Data Agnostic Client Samplings
  • 2.3 Advanced Client Sampling Techniques
  • 3 Convergence Guarantees
  • 3.1 Asymptotic FL Convergence with Respect to Client Sampling
  • 3.2 Application to Current Client Sampling Schemes
  • 4 Experiments on Real Data
  • 5 Conclusion
  • References.
  • Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration
  • 1 Introduction
  • 2 Related Work
  • 3 Method
  • 3.1 Non-IID Data
  • 3.2 DAC: Decentralized Adaptive Clustering
  • 3.3 Variable DAC
  • 4 Experimental Setup
  • 5 Results on Covariate Shift
  • 6 Results on Label Shift
  • 7 Conclusions
  • References
  • Sketch to Skip and Select: Communication Efficient Federated Learning Using Locality Sensitive Hashing
  • 1 Introduction
  • 2 Related Work
  • 3 Methods
  • 3.1 Sketch-Based Communication Skipping: Sketch-to-Skip
  • 3.2 Sketch-Based Client Selection: Sketch-to-Select.
  • 3.3 Sketch to Skip and Select FL Algorithm
  • 4 Experiments
  • 4.1 Experimental Setup
  • 4.2 Results
  • 5 Conclusions
  • References
  • Fast Server Learning Rate Tuning for Coded Federated Dropout
  • 1 Introduction
  • 2 Background
  • 3 Methodology
  • 3.1 Fast Server Learning Rate Adaptation
  • 3.2 Coded Federated Dropout
  • 4 Evaluation
  • 5 Conclusion and Future Works
  • References
  • FedAUXfdp: Differentially Private One-Shot Federated Distillation
  • 1 Introduction
  • 2 Related Work
  • 3 FedAUX
  • 3.1 Method
  • 3.2 Privacy
  • 4 FedAUXfdp
  • 4.1 Regularized Empirical Risk Minimization.

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