Federated learning in computational pathology: a literature review

Abstract

FL holds significant promise for enabling secure, privacy-preserving collaboration in healthcare, particularly within computational pathology. The reviewed studies highlight the feasibility of applying FL across diverse data types without the need to centralize sensitive information. Nevertheless, key challenges such as system interoperability, data heterogeneity, and model interpretability continue to hinder real-world adoption. Future research should focus on developing scalable, standardized FL infrastructures, improving model robustness across heterogeneous sources, and addressing ethical concerns around fairness and accountability to support safe and effective clinical integration.

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