Freeing Clients while Enhancing Large Models via Federated Weak-to-Strong Learning

Federated fine-tuning enables large models (LMs) to adapt to decentralized private data without raw data sharing. Existing parameter-efficient methods reduce the number of trainable parameters but still require clients to execute large model backbones, imposing substantial memory and computational overhead on resource-constrained devices. To overcome this limitation, we propose FedW2S, a federated weak-to-strong framework that frees up client computational bottlenecks and enhances large models. Instead of deploying a strong model locally, FedW2S transfers the knowledge learned from private data by lightweight weak models on the client side to a strong model on the server side. To mitigate unreliable supervision from weak models, FedW2S combines reliability-based filtering with discrepancy-aware target mixing, selectively absorbing informative weak knowledge while preserving useful knowledge encoded in the strong model. Compatibility-aware strong-to-weak feedback further enables bidirectional knowledge transfer, allowing server-side improvements to propagate back to weak clients and enhance their subsequent supervision. We also conduct a theoretical analysis of this. Experiments across visual and language tasks show that FedW2S achieves competitive strong model performance while significantly reducing the memory and computational requirements for client-side training. The code is available at: https://anonymous.4open.science/r/FedW2S.