31 citations · 58 across the 8 of their papers we have counts for
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cs.LG2022
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Liang Li, Chenpei Huang, Dian Shi +4
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to pr…
cs.LG2022★ 3 cited
Service Delay Minimization for Federated Learning over Mobile Devices
Rui Chen, Dian Shi, Xiaoqi Qin +3
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay eff…
cs.LG2022
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
Tian Liu, Jiahao Ding, Ting Wang +2
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) des…