Adapting Foundation Models (FMs) for down- stream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine- tune FM by allocating sub-FM to clients in FL, however, leading to suboptimal performance due to insufficient tuning and inevitable error accumula- tions of gradients. In this paper, we propose Feder- ated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modules. First, the sub-FM construction module employs a layer-wise com- pression approach, facilitating comprehensive FM fine-tuning across all layers by emphasizing those crucial neurons. Second, the sub-FM alignment module conducts a two-step distillations—layer- level and neuron-level—before and during FL fine- tuning respectively, to reduce error of gradient by accurately aligning sub-FM with FM under theo- retical guarantees. Experimental results on seven commonly used datasets (i.e., four text and three vi- sion) demonstrate the superiority of FedPFT. Our code is available at https://github.com/pzp-dzd/FedPFT.