Federated Learning for Next-Gen Computing Applications and Privacy-Preserving Medical Diagnosis

Abstract

As a new paradigm of decentralized artificial intelligence (AI), federated learning (FL) has taken the form of a revolution to deal with privacy of the data and efficiency of computational methods in contemporary applications. When compared to the conventional centralized machine learning techniques that necessitate the raw data to be relayed to a centralized server, FL allows cooperative model training on edge devices (e.g., smart phones, IoT-enabled sensors, and institutional servers) and never loses sensitive data. In this paper, the author examines how FL is integrated into the next generation of computing paradigm, i.e., edge computing, 6G-supported ultra-low latency communication, quantum-enhanced optimization to reach faster convergence, and AI accelerators to enable real-time inference on the edge. Much emphasis is made on using FL in such area as privacy-preserving medical diagnosis, which is still highly sensitive as there are severe regulatory and ethical issues on patient data. The paper engages in a complex investigation of the privacy-preserving methods which enlist the use of secure aggregation schemes, differential privacy schemes, and homomorphic encryption, that will in turn work to make the model resilient without violating privacy of individuals. Besides, the approaches to optimizing the models in terms of working with non-IID data and communication bottlenecks, as well as heterogeneity among the client devices, are discussed. Experimental testing is done on a variety of multi-institutional data consisting of medical imaging (CT scans, X-rays), wearable diagnostic sensors, and structured electronic health records (EHRs). Findings demonstrate that properly configured and private-sensitive layers allow the FL-based architectures to reach diagnosis accuracy similar to centralized models to drastically minimize the privacy risk and communication overhead. Additionally, performance measures such as accuracy, F1-score, AUC, and privacy leakage approximations affirm that FL provides a reasonable solution to sensitive and real-life application in the medical domain of AI. This contribution presents the prospects of FL as a foundational technology in next-gen computing systems and preconditions the formulation of subsequent works in the field of federated neuro-symbolic modeling, the blockchain-based system of audit trails, and explainable federated AI that complies with the standards of ethical AI implementation.