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.
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.
Bai, G., Li, Y., Li, Z., Zhao, L., & Kim, K. (2024). FedSpaLLM: Federated pruning of large language models. ArXiv Preprint ArXiv:2410.14852.
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.
Qiu, Y., Kim, K., & Yousefian, F. (2025). A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning. https://arxiv.org/abs/2504.01839