Publications

2026

1. IJCAI 2026

   A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation

   Zhiwei Li, Guodong Long, Chunxu Zhang, and 3 more authors

   In Proceedings of the 35th International Joint Conference on Artificial Intelligence (Survey Track), 2026

   Abs arXiv Bib 1

   This survey reframes the privacy challenge in federated recommendation from an architectural problem (data stays local) to a geometric one: gradient updates must be orthogonal to a sensitive subspace in the latent representation space. Building on this perspective, we organize the rapidly growing literature on Personalized Federated Foundation Models for recommendation into a unified framework, identify five open problems (semantic gap, structural rigidity, semantic heterogeneity, optimization conflict, compute and communication), and lay out five future directions that map one-to-one to those problems.

   @inproceedings{li2026pffm,
     title = {A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation},
     author = {Li, Zhiwei and Long, Guodong and Zhang, Chunxu and Zhang, Honglei and Zhang, Chengqi and Jiang, Jing},
     booktitle = {Proceedings of the 35th International Joint Conference on Artificial Intelligence (Survey Track)},
     year = {2026},
   }

2. AAAI 2026

   Federated Vision-Language-Recommendation with Personalized Fusion

   Zhiwei Li, Guodong Long, Jing Jiang, and 2 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2026

   Abs arXiv Bib Code 11

   FedVLR is a federated recommendation framework that integrates vision-language models with personalized fusion to address multimodal cold-start and heterogeneity. It uses adapter tuning on a frozen VLM backbone and a per-client fusion head that is trained locally, retaining global semantic priors while adapting to local taste distributions.

   @inproceedings{li2026fedvlr,
     title = {Federated Vision-Language-Recommendation with Personalized Fusion},
     author = {Li, Zhiwei and Long, Guodong and Jiang, Jing and Zhang, Chengqi and Yang, Qiang},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     year = {2026},
   }

2025

1. AAAI 2025

   

   Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

   Zhiwei Li, Guodong Long, Tianyi Zhou, and 2 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2025

   Abs arXiv Bib Code 39

   FedDAE casts personalized federated collaborative filtering as a variational autoencoder problem in which each client has its own decoder while sharing an encoder. This decomposition aligns the personalization-vs-generalization trade-off with the latent-space structure of the VAE and yields consistent gains across MovieLens, LastFM, and HetRec benchmarks.

   @inproceedings{li2025feddae,
     title = {Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach},
     author = {Li, Zhiwei and Long, Guodong and Zhou, Tianyi and Jiang, Jing and Zhang, Chengqi},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     year = {2025},
   }

2024

1. ICLR 2024

   Federated Recommendation with Additive Personalization

   Zhiwei Li, Guodong Long, and Tianyi Zhou

   In International Conference on Learning Representations, 2024

   Abs arXiv Bib HTML Code 93

   FedRAP decomposes a recommendation model into a globally shared component and a per-client additive personalization term, regularized to be sparse so that personalization is communicated efficiently. The method delivers state-of-the-art performance on standard federated recommendation benchmarks while using a fraction of the per-round communication.

   @inproceedings{li2024fedrap,
     title = {Federated Recommendation with Additive Personalization},
     author = {Li, Zhiwei and Long, Guodong and Zhou, Tianyi},
     booktitle = {International Conference on Learning Representations},
     year = {2024},
   }

2023

1. PRICAI 2023

   Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels

   Zhiwei Li, Zijian Yang, Lu Sun, and 2 more authors

   In Pacific Rim International Conference on Artificial Intelligence, 2023

   Abs arXiv Bib Code 3

   NAIL is a noise-aware multi-view weak-label learning framework that handles missing views, noisy features, and class imbalance jointly. It learns a shared low-dimensional embedding plus view-specific noise estimators, and balances training via class-aware reweighting.

   @inproceedings{li2023nail,
     title = {Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels},
     author = {Li, Zhiwei and Yang, Zijian and Sun, Lu and Kudo, Mineichi and Kimura, Keigo},
     booktitle = {Pacific Rim International Conference on Artificial Intelligence},
     year = {2023},
   }