arXiv Machine Learning October 8, 2026 By Koffka Khan

ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learning

Read the original on arXiv Machine Learning →

ORDERS is a personalized federated learning configuration that combines a shared backbone, private residual adapters, and a classifier, using geometric weights based on descending update norms, feature alignment, and private‑parameter perturbations. In extensive experiments on CIFAR‑10 and Sent140, ORDERS achieves slightly higher mean client accuracy than baseline methods, with a 5.47% reduction in parameter payload. Ablation studies show limited, dataset‑dependent benefits from norm ranking and alignment, and no clear advantage from perturbations.

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