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