TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning

ArXi:2509.26524v3 Announce Type: replace-cross In federated learning (FL), local personalization of models has received significant attention, yet personalized fine-tuning of foundation models remains underexplored. In particular, there is a lack of understanding in the literature on how to personalize foundation models in settings where there exist heterogeneity not only in data, but also in tasks and modalities across the clients. To address this gap, we propose Two-Stage Adaptive Personalization.