31 citations · 66 across the 5 of their papers we have counts for
5 papers
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…
Differentially Private and Fair Classification via Calibrated Functional Mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li +3
Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns re…