Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment.

ABSTRACT: Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated learning (FL) has been introduced to address these issues. However, FL faces challenges such as vulnerability to attacks, non-IID data, reliance on a central server, high communication overhead, and suboptimal model aggregation. Furthermore, FL is not optimized for realistic hospital database environments, where data are dynamically accumulated. To overcome these limitations, we propose federated influencer learning (FIL) as a secure and efficient collaborative learning paradigm. Unlike the server-client model of FL, FIL features an equal-stat

SUBMITTER: Chung H 

PROVIDER: S-EPMC11442468 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature