Enhanced Federated Learning Systems Enabling Secure, Privacy-Centric, and Distributed Model Training Across Cloud–Edge Layers

Abstract

The need to train distributed models on cloud and edge devices is of growing concern in terms of security, privacy, and efficiency. Federated learning (FL) is a potential solution to training machine learning models without causing data decentralization, but its implementation in cloud-edge systems creates issues like data heterogeneity, communication overhead, and susceptibility to security attacks. To counteract this, the paper introduces a stronger federated learning architecture that guarantees the security and privacy-friendly model training at cloud and edge layers. The frameworks are new algorithms, which take into consideration the latest encryption algorithms and differential privacy schemes, and optimization of communication protocols to provide better efficiency. This solution creates secure aggregation and a hybrid edge-cloud interaction model, and it reduces the risks of data leakage and unauthorized access to the information during the training process. The results of the experiments show that the improved federated learning framework attains a notable degree of balance concerning the model accuracy, privacy preservation, and communication cost, which is better in security and computational efficiency than the current federated learning frameworks. The present paper adds a powerful framework of safe, privacy-confidential zed, and distributed cloud-edge machine learning as a basis of future developments in the field.