Author

Xiong Li

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Conference Open access Sep 2026

Proactive Federated Unlearning: Sensitivity-Guided Sparse Adaptation on Key Layers

Driven by privacy regulations, federated unlearning (FU) aims to remove the influence of specific clients or samples from a trained federated model, approximating the behavior of retraining from scratch without the target data. However, existing FU methods are largely reactive: retraining-based solutions are accurate b...

Jin-Shan Lai, Feng-Chun Zhang, Yun-Yuan Wang et al. · Proceedings of the Thirty-Fi... · 0 citations

2026

A Byzantine-Robust Secure Federated Learning Scheme in Heterogeneous Data

Secure Federated Learning protects user privacy by keeping local gradients within the ciphertext space, thereby preventing direct exposure of raw gradients during aggregation. Paradoxically, this very protection introduces a fundamental vulnerability: the opacity of encrypted gradients blinds the server, rendering hone...

Jinbo Wang, Ruijin Wang, Zengpeng Li et al. · IEEE Transactions on Informa... · 0 citations