Alternating Distillation and Resource-Adaptive Pruning for Federated Large Model Adaptation.
This study delves into large PFMs adaptation in the resource-constrained federated learning environment, and proposes an innovative framework, namely ADRAP, which alternates between large model distillation and resource-adaptive pruning, with guaranteed convergence.