Yulin Zhao — accepted AI-conference papers

2025

Communication-efficient Verifiable and Oblivious Aggregation with Client Dropouts

Zhangshuang Guan, Yulin Zhao, Longyun Yang, Zhiguo Wan, Jinsong Han

ICASSP 2025accepted

Federated learning (FL) allows each client to train data locally and share only model parameters with an aggregation server. A critical component of FL is secure aggregation (SA), which protects user privacy during the server-side aggregation of client model parameters. However, SA-based FL still fa…

Cited by 0 Source ↗Scholar ↗

2024

Towards Effective and General Graph Unlearning via Mutual Evolution

Xunkai Li, Yulin Zhao, Zhengyu Wu, Wentao Zhang, Rong-Hua Li, Guoren Wang

AAAI 2024technical

With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving graph-based scenarios. However, most existing graph unlearning strategies primarily rely on well-designed architectures…

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