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

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devices, where edge nodes collaboratively train personalized models without sharing raw data. Although PFL addresses data heterogeneity by learning client-specific models, it still suffers from substantial uplink and downlink communication costs when exchanging high-dimensional parameters in bandwidth-constrained systems. Recent one-bit methods achieve extreme compression, but they usually rely on a single thresholding rule applied to the whole model. This design has two limitations. First

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