14 citations · 37 across the 8 of their papers we have counts for
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cs.LG2023★ 2 cited
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network
Hongda Wu, Ping Wang, C V Aswartha Narayana
Federated Learning (FL) enables many resource-limited devices to train a model collaboratively without data sharing. However, many existing works focus on model-homogeneous FL, whe…
cs.LG2021★ 14 cited
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data
Hongda Wu, Ping Wang
Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-inde…
cs.LG2020★ 8 cited
Fast-Convergent Federated Learning with Adaptive Weighting
Hongda Wu, Ping Wang
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive…