Federated learning in computational pathology: a literature review
Abstract
PURPOSE: Artificial intelligence has emerged as a powerful technique for data analysis and predictive modeling. However, traditional centralized learning methods, which require aggregating large and diverse datasets at a central location, present considerable privacy and security risks, particularly in sensitive areas such as healthcare. Federated learning (FL) offers a promising alternative by enabling collaborative model training without the need to share raw data. We aim to systematically examine the current state of the art in the application of FL within the healthcare domain, with a focus on computational pathology.
APPROACH: We conducted a systematic review of the published literature on FL in healthcare, with a specific focus on imaging-based applications relevant to computational pathology. Our analysis includes studies utilizing a range of medical imaging modalities such as whole-slide histopathology images, magnetic resonance imaging, computed tomography, and positron emission tomography. The selected studies were categorized based on a taxonomy of FL architectures, with a focus on understanding each study's motivations, implementation strategies, and targeted problems. We also evaluated recurring technical challenges such as system and data heterogeneity, privacy preservation mechanisms, and communication efficiency, as well as the integration of complementary technologies such as blockchain, homomorphic encryption, and multi-modal learning.
RESULTS: The…
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