Federated Deep Learning for Privacy-Preserving Prostate Cancer Diagnosis from Multicenter Multiparametric MRI Data
Keywords:
federated learning, prostate cancer, multiparametric MRI, privacy preservation, deep learning, multicenter study, medical imagingAbstract
Multiparametric magnetic resonance imaging has become a cornerstone in the diagnostic pathway for prostate cancer, yet the development of robust deep learning models for its interpretation is hampered by the fragmentation of sensitive imaging datasets across medical institutions. Federated learning offers a paradigm in which collaborative model training can occur without centralized data aggregation, preserving patient privacy while harnessing multicenter data diversity. This paper presents a comprehensive systems-level examination of federated deep learning architectures for privacy-preserving prostate cancer diagnosis using multiparametric MRI. We dissect the structural trade-offs inherent in designing federated topologies, aggregation strategies, and communication protocols that must contend with the high dimensionality and multimodal nature of prostate imaging. The analysis extends beyond algorithmic design to encompass privacy-enhancing technologies including differential privacy and secure aggregation, threat modeling against gradient leakage and inference attacks, and the intricate balance between utility and confidentiality. A substantial portion of the discussion is devoted to robustness and fairness across heterogeneous clinical sites, where variations in scanner vendors, imaging protocols, and patient demographics challenge the generalization guarantees of the global model. We further scrutinize the infrastructure demands for real-world multicenter deployment, addressing computational resource orchestration, longitudinal model maintenance, and integration with radiology workflows. Governance, regulatory compliance under GDPR and HIPAA, ethical accountability, and sustainable lifecycle management are framed as equally critical components of the federated ecosystem. By weaving together these technical and socio-technical dimensions, the paper articulates a systemic perspective that positions federated deep learning not merely as a distributed optimization technique but as a complex socio-technical infrastructure requiring careful architectural governance, transparent policy frameworks, and continuous cross-institutional stewardship to realize its potential for equitable and privacy-respecting prostate cancer diagnosis.
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