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arXiv Machine Learning By Ruizhe Huang, Chengran Li, Xiaochuan Shi

PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

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PACE introduces a propagation‑aware collaborative correction for one‑shot personalized federated graph learning. Each client sends a rank‑r update and a diagonal sketch of message moments, allowing the server to construct a correction that anchors to the receiver’s local model. A convex negative‑log‑likelihood calibration selects a single coefficient to blend local and external logits, improving accuracy and weighted‑F1 on most datasets while preserving local predictions when the correction is unhelpful.

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