We propose a novel and efficient federated learning framework named FedPAGE, where workers perform distributed pruning adaptively towards global efficiency.
Jun 18, 2024 · Abstract—When workers are heterogeneous in computing and transmission capabilities, the global efficiency of federated learning suffers from ...
Dec 12, 2023 · We propose a novel and efficient federated learning framework named FedPAGE, where workers perform distributed pruning adaptively towards global ...
We propose a novel and efficient federated learning framework named FedPAGE, where workers perform distributed pruning adaptively towards global efficiency, ...
Jul 5, 2024 · FedPAGE: Pruning Adaptively Toward Global Efficiency of Heterogeneous Federated Learning. IEEE/ACM Trans. Netw. 32(3): 1873-1887 (2024); 2023.
FedPAGE: Pruning Adaptively Toward Global Efficiency of Heterogeneous Federated Learning. IEEE/ACM Transactions on Networking. 2024-06 | Journal article. DOI ...
Feb 9, 2023 · We propose adaptive Dynamic Pruning for efficient Federated learning (FedDP). Specifically, we introduce a dynamic pruning component on each ...
Missing: FedPAGE: | Show results with:FedPAGE:
When workers are heterogeneous in computing and transmission capabilities, the global efficiency of federated learning suffers from the straggler issue, i.e., ...
FedPAGE: Pruning Adaptively Toward Global Efficiency of Heterogeneous Federated Learning. Article. Jan 2023. Guangmeng Zhou · Qi Li · Yang Liu · Ke Xu. When ...
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FedPAGE: Pruning Adaptively Toward. Global Efficiency of Heterogeneous. Federated Learning. G. Zhou, Q. Li, Y. Liu, Y. Zhao, Q. Tan, S. Yao, and K. Xu. 1888.