Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

ABSTRACT: Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are trans

SUBMITTER: Truhn D 

PROVIDER: S-EPMC10804934 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature