2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation
Linping Qu, Shenghui Song, Chi-Ying Tsui
In wireless federated learning (FL), the clients need to transmit the high-dimensional deep neural network (DNN) parameters through bandwidth-limited channels, which causes the com…
The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning
Zhijie Xie, Shenghui Song
In the context of Federated Reinforcement Learning (FRL), applying Proximal Policy Optimization (PPO) faces challenges related to the update order of its actor and critic due to th…
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
Linping Qu, Shenghui Song, Chi-Ying Tsui
Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy.…
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning
Ho Man Kwan, Shenghui Song
Recently, innovative model aggregation methods based on knowledge distillation (KD) have been proposed for federated learning (FL). These methods not only improved the robustness o…
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels
Linping Qu, Shenghui Song, Chi-Ying Tsui +1
Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wire…
Client Selection for Federated Policy Optimization with Environment Heterogeneity
Zhijie Xie, Shenghui Song
The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods that gained both theoretical…