Federated Learning for Privacy-Preserving Healthcare Diagnostics: Challenges and Emerging Solutions

Abstract

Federated Learning (FL) has become a groundbreaking approach for collaborative model training in the healthcare sector, allowing institutions to create effective diagnostic AI systems without the need to exchange sensitive patient information. This is especially crucial given the strict regulations like HIPAA and GDPR, which limit centralized data collection due to privacy concerns. This paper offers an in-depth examination of FL's applications in healthcare diagnostics, with a focus on areas such as medical imaging (MRI, CT, pathology), electronic health records (EHR), and multimodal diagnostics. We explore significant architectures including FedAvg, personalized FL, and hierarchical FL, along with privacy-enhancing technologies like differential privacy, secure aggregation, and homomorphic encryption. Additionally, we address fundamental challenges such as data heterogeneity (non-IID distributions), communication efficiency, model convergence, security threats, and clinical validation. By systematically reviewing over 100 recent studies from 2020 to 2026, we emphasize achievements in areas like brain tumor segmentation, diabetic retinopathy detection, and COVID-19 diagnostics, where FL models frequently match the performance of centralized models (Dice scores >0.85 in multi-institutional contexts). We also discuss emerging solutions such as blockchain for incentive mechanisms, edge computing for low-latency inference, and uncertainty quantification for reliable predictions. Despite advancements, ongoing challenges in scalability, fairness, and real-world implementation persist. This work suggests a roadmap that integrates FL with large language models (LLMs) and causal inference to develop next-generation privacy-preserving diagnostics. The findings highlight FL's potential to advance precision medicine while maintaining ethical standards.