Federated Learning in Cloud & Edge Environments: A Secure and Efficient AI Training Approach

Abstract

By letting models choose knowledge from distributed data sources without sending sensitive information to a central server, federated learning is transforming their AI training. This approach reduces information breach & unauthorized access concerns by maintaining raw data on the local devices, hence improving privacy. By just communicating encrypted model updates by FL allows many devices or edge nodes to cooperate with the train AI models instead of aggregating the information in a single place. In sectors such as healthcare, finance & IoT—where data sensitivity is more crucial—this is particularly important. Combining federated learning with cloud-edge architectures improves its performance by leveraging the edge devices for immediate learning & cloud computing capabilities for coordination. Notwithstanding these benefits, federated learning has security concerns including adversarial attacks, data poisoning & flaws in the model updates. To allay these issues, new solutions like safe aggregation, differential privacy & blockchain-based authentication are in development. FL provides a scalable & safe platform for distributed AI by combining durable cloud-edge architecture with privacy-preserving techniques, therefore enabling the responsible and effective use of ML