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 APPFLx.link.

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]

  • [Preprint] S. Bose, Y. Zhang, and K. Kim, “Addressing heterogeneity in federated load forecasting with personalization layers,” arXiv preprint arXiv:2404.01517, 2024. [Paper]

  • [CiSE 2024] 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]

2023#

  • [Preprint] G. Wilkins, S. Di, J. C. Calhoun, K. Kim, R. Underwood, and F. Cappello, “Efficient communication in federated learning using floating-point lossy compression,” arXiv preprint arXiv:2312.13461, 2023. [Paper]

  • [Preprint] 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,” arXiv preprint arXiv:2312.08701, 2023. [Paper]

  • [Preprint] S. Bose, Y. Zhang, and K. Kim, “Privacy-preserving load forecasting via personalized model obfuscation,” arXiv preprint arXiv:2312.00036, 2023. [Paper]

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

  • [e-Science 2024] 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]

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]