73 citations · 73 across the 2 of their papers we have counts for
3 papers
cs.LG2023
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Zhiying Feng, Xu Chen, Qiong Wu +3
Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. M…
cs.MA2023★ 73 cited
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
Yihong Li, Xiaoxi Zhang, Tianyu Zeng +4
Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each ta…
eess.SP2022
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning
Yinan Zou, Zixin Wang, Xu Chen +2
In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perfo…