8 citations · 24 across the 22 of their papers we have counts for
4 papers · 1 filter
Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning
Liwei Wang, Jun Li, Wen Chen +2
Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. Howeve…
Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Xuefeng Han, Wen Chen, Jun Li +5
Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. H…
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
Kecheng Fan, Wen Chen, Jun Li +3
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggrega…
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Yifan Shi, Kang Wei, Li Shen +4
Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication,…