FedReason: Large Language Models as Weight-Space Meta-Aggregators for Heterogeneous Federated Learning

Reported by arXiv cs.CR (Ali Mohammadi Ruzbahani, Abbas Yazdinejad, Hadis Karimipour)

Summary by Cyber Security News6 Nov 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2611.08383

arXiv:2611.08383v1 Announce Type: new Abstract: Federated learning servers combine client models with a fixed aggregation rule, and each rule in common use is designed for one kind of heterogeneity. We study whether a large language model (LLM) that inspects the client models can choose the rule itself, without any training on the server. In FedReason, the LLM is called every ten rounds.

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