Federated Learning Architectures For Privacy-Preserving Distributed Intelligence

Abstract

Privacy concerns in distributed intelligence systems have intensified with large-scale data sharing. Existing federated architectures often emphasize parameter exchange efficiency while insufficiently addressing adaptive, architecture-level privacy control across heterogeneous environments. This research aims to design robust Federated Learning (FL) architectures for privacy-preserving distributed intelligence using a novel Privacy-Aware Federated Stochastic Gradient Descent (PA-FedSGD) model. A Privacy-Aware Federated Edge dataset is collected from decentralized edge devices such as mobile sensors and Internet of Things (IoT) nodes, capturing diverse behavioral and contextual features, which contains 5,634 records. Data preprocessing involves Z-score normalization, Kalman filter-based noise filtering with adaptive scaling, and outlier detection techniques to enhance data quality, reduce variability, and improve stability in FL environments. The proposed model enables decentralized data retention, local model training, and secure parameter transmission through lightweight encryption and differential privacy mechanisms. PA-FedSGD is designed to optimize model convergence while embedding privacy-awareness into gradient updates, selectively masking sensitive gradients and regulating information leakage during aggregation. It ensures stable learning across heterogeneous nodes, while maintaining confidentiality constraints. The architecture integrates secure aggregation protocols and adaptive weighting strategies to enhance robustness and scalability. Performance evaluation demonstrates improved privacy preservation, communication efficiency, convergence stability, and resilience against inference risks. Achieved 95% accuracy with improved privacy, stability, and efficiency performance. The architecture establishes a scalable and adaptive foundation for secure distributed intelligence across real-world federated environments.