FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training

Yen Jung Chiu, Chao Chun Chuang, Yu Tai Wang, Lin Chi Yeh, Romel Edwardo Rudon, Kuan Wei Lin, Wei Jong Yang, Yang C. Fann, Pau Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages314-315
Number of pages2
ISBN (Electronic)9798350339840
DOIs
Publication statusPublished - 2023
Event2023 IEEE Conference on Artificial Intelligence, CAI 2023 - Santa Clara, United States
Duration: 2023 Jun 52023 Jun 6

Publication series

NameProceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023

Conference

Conference2023 IEEE Conference on Artificial Intelligence, CAI 2023
Country/TerritoryUnited States
CitySanta Clara
Period23-06-0523-06-06

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Modelling and Simulation
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training'. Together they form a unique fingerprint.

Cite this