Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
Overcoming Noisy and Irrelevant Data in Federated Learning
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Reliable Federated Learning for Mobile Networks
Active Federated Learning
Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
FedMD: Heterogenous Federated Learning via Model Distillation