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arXiv AI By Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza, Nicholas D. Lane, Leandro A. Villas

Task-Centric Personalized Federated Fine-Tuning of Language Models

Read the original on arXiv AI →

The paper introduces FedRouter, a personalized federated learning framework that clusters tasks rather than clients to build specialized language models. It employs local and global clustering of adapters to associate them with specific tasks and includes an evaluation router that directs test samples to the most suitable adapter. Experiments on a multitask dataset show FedRouter outperforms existing methods, achieving up to 6.1% relative improvement under task interference and 136% under generalization scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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