Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets

arXiv:2610.20753v1 Announce Type: cross Abstract: Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance gains will require increasing amounts of data and compute. Federated Learning (FL) facilitates training on decentralized private data, but client resource limitations can make training impractical, particularly as model sizes continue to expand. These resource constraints make the assumption of a shared server-client architecture impractical. Model heterogeneous FL offers a promising alternative by

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