@inproceedings{dfb6bd92c45846438c8853bd2f8fad14,
title = "FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training",
abstract = "Federated Learning (FL) provides an approach for performing the collaborative training of AI models without compromising data privacy. However, traditional implementations of FL require complex deployment strategies, making it challenging to perform training across multiple data centers. To address this issue, this article presents a platform designated as Federated Learning Agent (FLAg) which allows users to delegate their federated learning tasks to an automated process. FLAg features digital pathology databases as well as multiple pretrained models. The security of FLAg is ensured through its deployment on an HPC system and bastion host. While FLAg itself only offers FL training services, users may perform data annotation and inference through its integration with a proprietary ALOVAS AI pathology platform for a complete end-to-end process which includes annotation, FL training, and inference.",
author = "Chiu, {Yen Jung} and Chuang, {Chao Chun} and Wang, {Yu Tai} and Yeh, {Lin Chi} and {Edwardo Rudon}, Romel and Lin, {Kuan Wei} and Yang, {Wei Jong} and Fann, {Yang C.} and Chung, {Pau Choo}",
note = "Funding Information: This work was supported by Taiwan's NSTC under grants 111-2634-F-006-012 and 112-2327-B-468-001. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference on Artificial Intelligence, CAI 2023 ; Conference date: 05-06-2023 Through 06-06-2023",
year = "2023",
doi = "10.1109/CAI54212.2023.00139",
language = "English",
series = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "314--315",
booktitle = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023",
address = "United States",
}