Publications

This page presents the publications using the APPFL framework. You may also find an FL-as-a-service platform built on top of APPFL at service.appfl.ai.

2026

  • [ICML 2026] Y. Li, E. Dey, Z. Li, K. Raghavan, R. Madduri, K. Kim, “FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training,” in The Forty-Third International Conference on Machine Learning, 2026. [Paper]

  • [eScience 2026] Z. Li, A. Chunduru, H. Krishnan, E. Chagnon, P. Nugent, K. Kim, R. Madduri, “Federated Learning on the American Science Cloud using APPFL,” to appear in 2026 IEEE 22nd International Conference on e-Science (eScience), 2026.

  • [eScience 2026][Poster] A. Chunduru, M. Joel, Z. Li, R. Madduri, “Towards Global Federated Genome-Wide Association Meta-Analysis Using GA4GH TES,” to appear in 2026 IEEE 22nd International Conference on e-Science (eScience), 2026.

  • [Preprint] Y. Li, Z. Li, K. Chard, I. Foster, T. Munson, R. Madduri, K. Kim, “Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers,” arXiv preprint arXiv:2603.19544, 2026. [Paper]

2025

  • [CCGrid 2025] Z. Li, S. He, Z. Yang, M. Ryu, K. Kim, R. Madduri, “Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework,” in 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2025. [Paper]

  • [NAACL Main 2025] G. Bai, Y. Li, Z. Li, L. Zhao, K. Kim, “FedSpaLLM: Federated Pruning of Large Language Models,” in The Main Conference of 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025 Main), 2025. [Paper]

  • [CSBJ][Journal] T.-H. Hoang, J. Fuhrman, R. Madduri, M. Li, P. Chaturvedi, Z. Li, K. Kim, M. Ryu, R. Chard, E. Huerta et al., “Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with appflx,” in Computational and Structural Biotechnology Journal, 2025. [Paper]

  • [eScience 2025] K. Hiniduma, Z. Li, A. Sinha, R. Madduri, S. Byna, “CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning”, to appear in 2025 IEEE 21st International Conference on e-Science (eScience), 2025. [Paper]

  • [Allerton 2025] A. Sinha, Z. Li, T. Liu, V. Kindratenko, K. Kim, R. Madduri, “FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud”, to appear in 61st Allerton Conference on Communication, Control, and Computing, 2025. [Paper]

  • [TPS-ISA 2025] Z. Li, A. Sinha, Y. Li, K. Chard, K. Kim, R. Madduri, “Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science,” in Proceedings of the 7th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA 2025), 2025. [Paper]

  • [ApJS][Journal] P. Patel, A. Corsi, E. A. Huerta, K. Merfeld, V. Tiki, Z. Li, et al., “RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up,” to appear in The Astrophysical Journal Supplement Series, 2025. [Paper]

  • [SEC 2025] Z. Chen, W. Chen, D. Zhang, K. Kim, G. Li, S. Di, X. Lu, “FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems,” in Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing (SEC), 2025, pp. 1-16.

2024

  • [ICLR 2024] Z. Li, P. Chaturvedi, S. He, H. Chen, G. Singh, V. Kindratenko, E. A. Huerta, K. Kim, and R. Madduri, “Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,” in The Twelfth International Conference on Learning Representations, 2024. [Paper]

  • [ICDCS 2024] G. Wilkins, S. Di, J. C. Calhoun, Z. Li, K. Kim, R. Underwood, and F. Cappello, “Efficient communication in federated learning using floating-point lossy compression,” in International Conference on Distributed Computing Systems, 2024. [Paper]

  • [CiSE 2024][Journal] Z. Li, S. He, P. Chaturvedi, V. Kindratenko, E. A. Huerta, K. Kim, and R. Madduri, “Secure federated learning across heterogeneous cloud and high-performance computing resources - a case study on federated fine-tuning of llama 2,” in Computing in Science & Engineering, 2024. [Paper]

  • [IEEE PES 2024] S. Bose, Y. Zhang, and K. Kim, “Privacy-preserving load forecasting via personalized model obfuscation,” in 2024 IEEE PES General Meeting, 2024. [Paper]

  • [IISE 2024] S. Bose, Y. Zhang, and K. Kim, “Addressing heterogeneity in federated load forecasting with personalization layers,” in The Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo, 2024. [Paper]

  • [IEEE TPS 2024] R. Madduri, Z. Li, T. Nandi, K. Kim, M. Ryu, A. Rodriguez, “Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System,” in The Sixth IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, 2024. [Paper]

  • [IEEE BigData 2024] K. Kim, et al. “Privacy-Preserving Federated Learning for Science: Challenges and Research Directions,” in 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. [Paper]

  • [SSDBM 2024] K. Hiniduma, S. Byna, J. L. Bez, and R. Madduri. “AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI,” in Proceedings of the 36th International Conference on Scientific and Statistical Database Management, 2024. [Paper]

  • [Preprint] C. Iakovidou, K. Kim, “Asynchronous Federated Stochastic Optimization with Exact Averaging for Heterogeneous Local Objectives,” arXiv preprint arXiv:2405.10123, 2024. [Paper]

2023

  • [e-Science 2023] Z. Li, S. He, P. Chaturvedi, T.-H. Hoang, M. Ryu, E. Huerta, V. Kindratenko, J. Fuhrman, M. Giger, R. Chard et al., “APPFLx: Providing privacy-preserving cross-silo federated learning as a service,” in 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE, 2023, pp. 1-4. [Paper] [Web Service]

  • [Preprint] S. Bose and K. Kim, “Federated short-term load forecasting with personalization layers for heterogeneous clients,” arXiv preprint arXiv:2309.13194, 2023. [Paper]

2022

  • [IPDPSW 2022] M. Ryu, Y. Kim, K. Kim, and R. K. Madduri, “APPFL: open-source software framework for privacy-preserving federated learning,” in 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2022, pp. 1074-1083. [Paper]