31 citations · 49 across the 3 of their papers we have counts for
4 papers · 1 filter
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li, Guoliang Cheng, Xumin Huang +4
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjus…
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing
Peichun Li, Xumin Huang, Miao Pan +1
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied…
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
Pavana Prakash, Jiahao Ding, Maoqiang Wu +3
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In…
Evaluation of Inference Attack Models for Deep Learning on Medical Data
Maoqiang Wu, Xinyue Zhang, Jiahao Ding +4
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained f…