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arXiv Machine Learning By Xu Zhang, Xingyu Hou, Jiacheng Cheng, Kaiyuan Feng, Maoguo Gong

Communication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching

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The paper introduces a communication‑efficient personalized federated learning framework that uses layer‑wise multi‑threshold random sketching. By assigning each neural network layer its own set of quantization thresholds, the method adapts to layer‑specific parameter distributions and provides a finer low‑bit representation than single‑threshold one‑bit compression. This approach supports bidirectional communication with compact sketches and improves the communication‑accuracy tradeoff over existing one‑bit methods.

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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