FedHydra: Toward Parameter-Efficient and Backdoor-Resistant Federated Unlearning in Human-Centric Metaverse Service
Li Juan Duan, Zhenpeng Li, Xiaohan Yuan, Wei Wang, Jiqiang Liu, Wei Ni · IEEE Transactions on Services Computing · 2025
Federated learning significantly enhances immersive user experiences in the human-centric metaverse by training machine learning models while keeping distributed data localized. As the right to be forgotten is legislated globally, users in the human-centric metaverse should have the option to have their data forgotten. Federated Unlearning (FU) is an emerging solution that enables the server to remove client contributions from trained global models by calibrating the historical model parameters of participating clients. However, retaining the entire model parameters incurs significant storage overhead, and the involvement of the remaining clients exposes the global model to the risk of backdoor attacks. This paper presents a new parameter-efficient and backdoor-resistant FU framework, called FedHydra. FedHydra segments the model into a base and a classifier layer. By storing the classifier parameters critical to predicting output, FedHydra reduces the storage overhead of the server. FedHydra also calibrates the classifier parameters and compares cosine similarity between the old and new classifier parameters of the remaining clients, reducing the computational overhead of unlearning while effectively preventing the abnormal clients from participating in the calibration. Experiments show that, compared with the full parameter-based storage calibration method, FedHydra achieves comparable unlearning effects, re duces storage overhead on the server, and improves unlearning efficiency. In addition, FedHydra reduces the attack success rates in different backdoor attack settings while maintaining the predictive performance of the global model.