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publications

FedMeS: Personalized Federated Continual Learning Leveraging Local Memory

Preprint, arXiv.

A personalized federated continual learning framework that leverages local memory to address client drift and catastrophic forgetting.

arXiv

BibTeX

@article{xie2024fedmes,
  title = {FedMeS: Personalized Federated Continual Learning Leveraging Local Memory},
  author = {Xie, Jin and Zhu, Chenqing and Li, Songze},
  journal = {arXiv preprint arXiv:2404.12710},
  year = {2024}
}

Client-Driven Federated Learning for Dynamic Mixtures of Distributions

Accepted to WASA 2026.

An asynchronous personalized federated learning framework centered on client-side adaptation demands.

Conference Version | arXiv

BibTeX

@inproceedings{zhu2026clientdriven,
  title = {Client-Driven Federated Learning for Dynamic Mixtures of Distributions},
  author = {Zhu, Chenqing and Li, Songze},
  booktitle = {Proceedings of the 21st International Conference on Wireless Algorithms, Systems, and Applications (WASA 2026)},
  year = {2026},
  note = {To appear}
}

When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems

Accepted to 35th USENIX Security Symposium (USENIX Security '26).

A data-free and stealthy server-side backdoor attack against federated LLM-based question-answering systems.

arXiv

BibTeX

@inproceedings{zhu2026aggregator,
  title = {When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems},
  author = {Zhu, Chenqing and Dai, Yanbo and Tian, Yulong and Li, Qingming and Li, Songze},
  booktitle = {Proceedings of the 35th USENIX Security Symposium},
  year = {2026},
  note = {To appear}
}