5 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +5
Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The conce…
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
Hanlin Gu, Gongxi Zhu, Jie Zhang +4
In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotte…
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
Gongxi Zhu, Donghao Li, Hanlin Gu +3
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership…
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature
Wenyuan Yang, Gongxi Zhu, Yuguo Yin +4
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. The high cost of training and the significant value of the glob…
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
Wenyuan Yang, Yuguo Yin, Gongxi Zhu +4
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models…