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 <https://appflx.link/>`_.

2025
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- **[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] <https://arxiv.org/pdf/2409.11585>`_
- **[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] <https://arxiv.org/pdf/2410.14852>`_
- **[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] <https://doi.org/10.1016/j.csbj.2024.12.001>`_
- **[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] <https://arxiv.org/pdf/2505.23849>`_
- **[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] <https://arxiv.org/pdf/2505.21727>`_
- **[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] <https://arxiv.org/abs/2507.14827>`_


2024
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- **[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] <https://openreview.net/forum?id=msXxrttLOi>`_
- **[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] <https://arxiv.org/pdf/2312.13461.pdf>`_
- **[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] <https://arxiv.org/pdf/2402.12271.pdf>`_
- **[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] <https://arxiv.org/pdf/2312.00036.pdf>`_
- **[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] <https://arxiv.org/pdf/2404.01517.pdf>`_
- **[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] <https://arxiv.org/pdf/2409.19756>`_
- **[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] <https://ieeexplore.ieee.org/document/10825853>`_
- **[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] <https://dl.acm.org/doi/pdf/10.1145/3676288.3676296>`_
- **[Preprint]** C. Iakovidou, K. Kim, "Asynchronous Federated Stochastic Optimization with Exact Averaging for Heterogeneous Local Objectives," *arXiv preprint arXiv:2405.10123*, 2024. `[Paper] <https://arxiv.org/pdf/2405.10123>`_

2023
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- **[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] <https://arxiv.org/pdf/2308.08786.pdf>`_  `[Web Service] <https://appflx.link/>`_
- **[Preprint]** S. Bose and K. Kim, "Federated short-term load forecasting with personalization layers for heterogeneous clients," *arXiv preprint arXiv:2309.13194*, 2023. `[Paper] <https://arxiv.org/pdf/2309.13194.pdf>`_

2022
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- **[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] <https://arxiv.org/pdf/2202.03672.pdf>`_