- Federated Learning Based on Dynamic Regularization
- Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms.
- FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
- HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
- Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
- FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
- Adaptive Federated Optimization
- Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
- FedMix: Approximation of Mixup under Mean Augmented Federated Learning.
- Personalized Federated Learning with First Order Model Optimization