Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients

ArXi:2506.11024v4 Announce Type: replace As AI becomes personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases. Personalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods remain confined to rather simplified scenarios where data and models are the same across clients.