2 papers
cs.LG2026
FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client
Gongxi Zhu, Hanlin Gu, Lixin Fan +2
One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server-side foundation model. Exis…
cs.DC2024
Disentangling data distribution for Federated Learning
Xinyuan Zhao, Hanlin Gu, Lixin Fan +2
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data pr…