Comparative Analysis of Federated Learning Architectures, Algorithms, and Privacy-Preserving Techniques for Healthcare Applications | IJCT Volume 13 – Issue 3 | IJCT-V13I3P90
International Journal of Computer Techniques
ISSN 2394-2231
Author
Tejas H V, Sachin K, Kishore Kumar K, Monica K P
Abstract
The deployment of artificial intelligence in healthcare presents a critical tension between leveraging datasets for medical advancement and adhering to stringent privacy regulations such as HIPAA and GDPR. Federated Learning (FL) offers a distributed architecture that enables institutions to collaboratively train models without exchanging sensitive, raw patient data. This paper presents a comparative analysis of FL frameworks for privacy-preserving predictive analytics, evaluating core architectures—Horizontal FL, Vertical FL, and Federated Transfer Learning —alongside the performance of algorithms like FedAvg, FedSGD, and FedProx in navigating the challenges of non-independent and identically distributed (non-IID) clinical data. Furthermore, the study quantifies the effectiveness of privacy-preserving techniques, including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMPC) , against threat vectors such as model inversion, backdoor, and data poisoning attacks. Our findings indicate that while advanced algorithms like FedProx significantly enhance stability in heterogeneous environments , hybrid privacy mechanisms offer the strongest defense against adversarial threats. However, these integrated frameworks introduce inherent trade-offs between privacy guarantees, computational overhead, and model utility. Ultimately, no single FL algorithm or privacy technique is universally optimal ; successful clinical deployment requires context-aware frameworks that carefully balance architectural design, data modality, and regulatory compliance to achieve reliable predictive analytics.
Keywords
Federated Learning (FL), Privacy-Preserving Machine Learning, Healthcare Artificial Intelligence, Predictive Models, Data Heterogeneity (Non-IID), Differential Privacy (DP), Homomorphic Encryption (HE), Regulatory Compliance (HIPAA, GDPR).
Conclusion
In conclusion, the deployment of federated learning in healthcare offers a transformative, decentralized paradigm that successfully balances the advancement of collaborative medical AI with the stringent privacy mandates of HIPAA and GDPR. This comparative analysis demonstrates that while advanced algorithms like FedProx effectively mitigate the performance degradation caused by non-IID clinical data , the base architecture must be augmented with hybrid privacy-preserving techniques—such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation—to defend against sophisticated adversarial threats like model inversion and data poisoning. However, implementing these robust cryptographic defenses introduces a critical “privacy-performance-practicality” triangle, demanding careful calibration between mathematical privacy guarantees, computational execution times, and downstream model accuracy. Ultimately, because no single algorithm or privacy mechanism is universally optimal, the future of clinical federated learning lies in the maturation of context-aware, integrated, and hybrid frameworks that optimize specific data modalities and resource constraints to realize secure, auditable, and scalable predictive analytics. Moving forward, the successful translation of these frameworks from theory to production will rely on fostering institutional trust and establishing standardized governance models alongside technical advancements. As healthcare networks expand, defining transparent rules for data contribution, collaborative ownership, and multi-tier network accountability will be just as critical as the cryptographic protocols securing them. By unifying robust data regularization algorithms like FedProx, verifiable blockchain audit trails, and multi-layered hybrid privacy defenses, the medical community can confidently move past isolated data silos. Ultimately, these integrated federated learning systems will lay the groundwork for a secure, globally interconnected digital health ecosystem, driving clinical innovation and improving patient outcomes while keeping patient data entirely confidential and protected at its source.
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How to Cite This Paper
Tejas H V, Sachin K, Kishore Kumar K, Monica K P (2026). Comparative Analysis of Federated Learning Architectures, Algorithms, and Privacy-Preserving Techniques for Healthcare Applications. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
Comparative Analysis of Federated Learning Architectures, Algorithms, and Privacy-Preserving Techniques for Healthcare ApplicationsDownload
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