A Privacy-Preserving Federated Learning Framework for Secure Distributed Artificial Intelligence Applications
Keywords:
federated learning, differential privacy, secure aggregation, distributed artificial intelligence, data governance, algorithmic fairness, robustness, sustainabilityAbstract
Distributed artificial intelligence systems increasingly rely on sensitive data held by multiple organizations, devices, and administrative domains. Centralizing these data creates substantial privacy, security, and regulatory risks. Federated learning has emerged as a compelling architectural alternative because it enables collaborative model training without direct raw data transfer. However, federated learning alone does not guarantee privacy. Model updates, aggregate statistics, and global model parameters can leak information about local training records, while malicious clients can attempt to poison or backdoor the shared model. This paper presents a system-level privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, cryptographic isolation, robust update management, and governance-aware deployment controls. Rather than treating privacy as a single algorithmic addition, the framework conceptualizes privacy protection as a layered socio-technical infrastructure. The discussion examines structural trade-offs among communication efficiency, model accuracy, client heterogeneity, privacy guarantees, robustness, fairness, regulatory compliance, and long-term sustainability. The paper further analyzes cross-domain deployment considerations for healthcare, finance, public services, and mobile computing, emphasizing that privacy-preserving federated learning must be evaluated not only through technical metrics but also through institutional accountability, fairness auditing, and lifecycle governance. By connecting systems architecture, privacy mechanisms, and policy instruments, the paper offers a forward-looking research agenda for secure distributed artificial intelligence applications.
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