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arXiv Machine Learning By Jintao Yan, Tan Chen, Yuxuan Sun, Sheng Zhou, Zhisheng Niu

FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels

Read the original on arXiv Machine Learning →

FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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