#2021
- Debiasing Model Updates for Improving Personalized Federated Training
- Ditto: Fair and Robust Federated Learning Through Personalization
- Federated Learning under Arbitrary Communication Patterns
- One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
- Exploiting Shared Representations for Personalized Federated Learning
- Heterogeneity for the Win: One-Shot Federated Clustering
- Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
- Federated Learning of User Verification Models Without Sharing Embeddings
- FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
- Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
- Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
- Personalized Federated Learning using Hypernetworks
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks.
- Federated Continual Learning with Weighted Inter-client Transfer
- Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity.
- Federated Composite Optimization.
- Data-Free Knowledge Distillation for Heterogeneous Federated Learning
- Federated Learning with Only Positive Labels
- FetchSGD: Communication-Efficient Federated Learning with Sketching
- From Local SGD to Local Fixed-Point Methods for Federated Learning
- Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
- FedBoost: A Communication-Efficient Algorithm for Federated Learning
- Bayesian Nonparametric Federated Learning of Neural Networks
- Agnostic Federated Learning
- Analyzing Federated Learning through an Adversarial Lens�