A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.

ABSTRACT: Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a pathway forward for training on distributed medical datasets. Existing approaches typically require updates to a training model to be transferred to a central server, potentially breaching data privacy laws unless the updates are sufficiently disguised or abstracted to prevent reconstruction of the dataset. Here we present a completely decentralized federated learning approach, using knowledge distillation, ensuring data privacy and protection. Each node operates independently w

SUBMITTER: Nguyen TV 

PROVIDER: S-EPMC9133021 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature