Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models 事件

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models arXiv:2606.06154v1 Announce Type: new Abstract: Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA methods suffer from two fundamental limitations: (1) structural aggregation bias, where independently averaging low rank factors fails to approximate the true combined update, and (