Ruijin Wang — accepted AI-conference papers

2026

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

Jinshan Lai, Fengchun Zhang, Yunyuan Wang, Dongfen Li, Yang Zhang, Xiong Li +1

IJCAI 2026

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 solu

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2025

General Dynamic Regularization Federated Learning with Hybrid Sharpness-Aware Minimization

Fengchun Zhang, Dongfen Li, Jinshan Lai, Yang Zhang, Fengli Zhang, Ruijin Wang

ICASSP 2025accepted

One of the main challenges in federated learning is its non-independent and identically distributed (non-IID) nature, where independent client training leads to overfitting and model deviations, negatively impacting overall performance. To address this, most research focuses on aligning local and gl…

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