Enhancing patient data privacy in healthcare

Decentralised systems and federated learning (FL) have become viable options for safeguarding patient data in medical applications. By enabling collaborative model training without centralising sensitive medical data, these methods improve security and protect privacy. In the context of healthcare data security, this synthesis offers a summary of the main conclusions and difficulties related to FL and distributed systems.

Because federated learning (FL) keeps patient data local and autonomous within the organisations that generate it, it naturally secures patient data. This method preserves privacy by preventing participants from directly exchanging sensitive information(1–3). The confidentiality of medical records is further improved during the model aggregation phase(3) by sophisticated privacy-preserving strategies such as differential privacy and safe multi-party computation.

FL systems are not impervious to security threats, even with the privacy benefits. Data leaking during model updates and vulnerability to adversarial attacks are examples of potential weaknesses. To reduce these dangers, it is essential to implement strong security mechanisms like encryption and secure communication protocols(1,4,5).

Split Learning (SL) further enhances privacy by splitting the model training process between clients and servers, ensuring that raw data never leaves the local environment. Hybrid approaches, such as Split-Federated Learning (SFL), combine the benefits of both FL and SL to address specific challenges in healthcare data management(6).

Managing unbalanced datasets and sporadic client engagement is one of the major obstacles in decentralised healthcare systems. To solve these problems, data augmentation strategies and strong FL frameworks have been created, guaranteeing that the models continue to be accurate and dependable in spite of these difficulties(2,7).

FL has been used in a number of real-world healthcare situations, including disease prediction and tumour segmentation. These uses show that FL is both practical and efficient in preserving data privacy while attaining excellent model performance. As an illustration of the potential for global cooperation without sacrificing patient privacy, FL frameworks have been utilized to train models using brain tumor segmentation data from several nations(8).

Aligning FL with existing trust structures in healthcare systems is essential for its widespread adoption. Involving trusted stakeholders, such as clinicians, in the FL process can increase trust and facilitate smoother integration into healthcare workflows(7).

My future research suggests focusing on addressing technical challenges such as communication latency, system heterogeneity, and ensuring fairness in model training. Additionally, regulatory compliance and data ownership issues need to be carefully considered to facilitate the adoption of FL in healthcare(6,9).