Privacy-Preserving Federated Learning for Science

  1. Li, Z., He, S., Yang, Z., Ryu, M., Kim, K., & Madduri, R. (2024). Advances in APPFL: A comprehensive and extensible federated learning framework. ArXiv Preprint ArXiv:2409.11585.
  2. Madduri, R., Li, Z., Nandi, T., Kim, K., Ryu, M., & Rodriguez, A. (2024). Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System. 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), 273–279.
  3. Bai, G., Li, Y., Li, Z., Zhao, L., & Kim, K. (2024). FedSpaLLM: Federated pruning of large language models. ArXiv Preprint ArXiv:2410.14852.
  4. Kim, K., Raghavan, K., Kotevska, O., Dorier, M., Madduri, R., Ryu, M., Munson, T., Ross, R., Flynn, T., Kagawa, A., & others. (2024). Privacy-Preserving Federated Learning for Science: Challenges and Research Directions. 2024 IEEE International Conference on Big Data (BigData), 7849–7853.
  5. Qiu, Y., Kim, K., & Yousefian, F. (2025). A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning. https://arxiv.org/abs/2504.01839
  6. Ebrahimi, M., Shanbhag, U. V., & Yousefian, F. (2025). On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods. https://arxiv.org/abs/2505.22916
  7. Flynn, T., Johnstone, P., & Yoo, S. (2024). Problem-dependent convergence bounds for randomized linear gradient compression. https://arxiv.org/abs/2411.12898
  8. Xu, J., Hu, R., & Kotevska, O. (2025). Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy. https://arxiv.org/abs/2505.13655
  9. Xu, J., Hu, R., Kotevska, O., & Zhang, Z. (2025). Traceable Black-box Watermarks for Federated Learning. https://arxiv.org/abs/2505.13651
  10. Sinha, A., Li, Z., Liu, T., Kindratenko, V., Kim, K., & Madduri, R. (2025). FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud. ArXiv Preprint ArXiv:2505.21727.
  11. Byeon, G., Ryu, M., Di, Z. W., & Kim, K. (2025). FIRM: Federated Image Reconstruction using Multimodal Tomographic Data. ArXiv Preprint ArXiv:2501.05642.
  12. Van Nguyen, A., Klabjan, D., Ryu, M., Kim, K., & Di, Z. (2025). Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction. ArXiv Preprint ArXiv:2502.02761 (to Appear in IJCNN 2025).
  13. Hamann, H. F., Gjorgiev, B., Brunschwiler, T., Martins, L. S. A., Puech, A., Varbella, A., Weiss, J., Bernabe-Moreno, J., Massé, A. B., Choi, S. L., & others. (2024). Foundation models for the electric power grid. Joule, 8(12), 3245–3258.