Stefano Izzo

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.