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FedReason: Modelos de lenguaje grandes como metaagregadores de peso-espacio para el aprendizaje federado heterogéneo

arXiv:2611.08383v1 Tipo de anuncio: nuevo Resumen: Los servidores de aprendizaje federado combinan modelos de cliente con una regla de agregación fija, y cada regla de uso común está diseñada para un tipo de heterogeneidad. Estudiamos si a la

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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. It analyses the weights by running code in a restricted server-side sandbox, sees only the printed results, may score a few candidates on a 500-image proxy set, and submits a short Python aggregation program. The server applies the program until the next call and falls back to FedAvg