No one idles: Efficient heterogeneous federated learning with parallel edge and server computation

F Zhang, X Liu, S Lin, G Wu, X Zhou… - International …, 2023 - proceedings.mlr.press
Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …
被引用次数：7 相关文章

No one idles: efficient heterogeneous federated learning with parallel edge and server computation

F Zhang, X Liu, S Lin, G Wu, X Zhou, J Jiang… - Proceedings of the 40th …, 2023 - dl.acm.org
Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …

No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation

F Zhang, X Liu, S Lin, G Wu, X Zhou, J Jiang, X Ji - openreview.net
Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …
[PDF] mlr.press

[PDF][PDF] No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation

F Zhang, X Liu, S Lin, G Wu, X Zhou, J Jiang, X Ji - proceedings.mlr.press
Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …