Secure Aggregation Protocols in Federated AI for Anonymized Health Data

In the increasingly data-driven landscape of healthcare, the application of Federated Learning (FL) has emerged as a transformative paradigm, enabling the collaborative training of machine learning models across decentralized datasets while preserving data privacy. This approach is particularly pertinent for health data, which is often sensitive and subject to stringent regulatory requirements. However, the integration of secure aggregation protocols within Federated AI systems is crucial for ensuring the confidentiality and integrity of anonymized health data during the aggregation process. This paper comprehensively reviews the state of secure aggregation protocols in the context of Federated AI, emphasizing their role in safeguarding patient privacy while allowing for the effective utilization of health data. We categorize existing secure aggregation methods based on their cryptographic techniques, including homomorphic encryption, secure multiparty computation, and differential privacy, analyzing their strengths and limitations in practical applications. Furthermore, we explore the implications of these protocols on data utility, computational efficiency, and scalability in real-world healthcare settings. By synthesizing recent advancements and ongoing challenges in the field, this study underscores the importance of designing robust aggregation protocols that not only enhance security but also facilitate the seamless integration of diverse health data sources. We propose a framework for evaluating the performance of these protocols, taking into account factors such as communication overhead, resilience against attacks, and adaptability to various federated learning architectures. Our findings indicate that while significant progress has been made, there remains a critical need for ongoing research to balance the trade-offs between security, privacy, and model performance. This paper aims to contribute to the development of more sophisticated secure aggregation protocols that can effectively support the growing demand for collaborative, AI-driven health analytics without compromising patient confidentiality. Ultimately, we advocate for a multidisciplinary approach that incorporates insights from cryptography, data science, and healthcare policy to advance the secure and ethical use of federated AI in health data research.