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Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
Considering the device heterogeneity, and energy cost associated with parameter aggregation, in this paper, the problem on how to efficiently leverage the limited resources available to enhance the model performance is investigated.
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way.
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks
Specifically, we provide convergence bounds for both digital and analog transmission approaches, enabling analysis of the model performance trained on DFL.
Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates
local update) and that of aggregations (a. k. a.
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