Jul 14, 2021 · Specifically, AAFL can reduce the completion time by about 70 percent and improve the learning accuracy by about 28 percent under resource ...
AAFL can reduce the completion time by about 70 percent and improve the learning accuracy by about 28 percent under resource constraints.
In this paper, we address the problem of how to efficiently utilize the limited computation and communication resources at the edge for the optimal learning ...
Oct 22, 2024 · In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized ...
In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized ...
Nov 16, 2024 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
Nov 20, 2024 · The author in introduces FedSA, a semi-asynchronous federated learning technique for heterogeneous edge computing networks. FedSA allows edge ...
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This paper analyzes the convergence bound of distributed gradient descent from a theoretical point of view, and proposes a control algorithm that determines ...
Dec 14, 2024 · Adaptive Federated Learning in Resource Constrained Edge Computing Systems. April 2018. DOI:10.48550/arXiv.1804.05271. Authors: Shiqiang Wang ...
This paper proposes an “Asynchronous-Adaptive FL” (AAFL) scheme, which allows that medical devices with different performances have a heterogeneous number ...