121 citations · 306 across the 17 of their papers we have counts for
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cs.LG2022★ 2 cited
Benchmarking of DL Libraries and Models on Mobile Devices
Qiyang Zhang, Xiang Li, Xiangying Che +6
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms…
cs.LG2020★ 31 cited
Hierarchical Federated Learning through LAN-WAN Orchestration
Jinliang Yuan, Mengwei Xu, Xiao Ma +3
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL pro…
cs.LG2020★ 10 cited
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang, Qipeng Wang, Mengwei Xu +4
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is h…