Federated learning in cloud environments: Enhancing data privacy and AI model training across distributed systems

Federated Learning (FL) is a recently proposed machine learning scheme for decentralized training across distributed devices with enhanced data privacy. FL is known to provide a solution in cloud environments to overcome the privacy concerns arising out of centralized data collection. In this study, we investigate Federated Learning in cloud-based system, in particular, cognitive regarding its capability to secure data privacy, scalability and impact on model training by AI. Then, results were obtained in experiments to evaluate how FL performs, in terms of model accuracy and communication overhead, and in how scalable it is using publicly available datasets. Results of time to completion and accuracy across Federated Learning and centralized learning systems indicate that while there is a loss in accuracy from non IID data distribution, Federated Learning also exhibits advantages regarding scale (scaling order) and privacy. Communication costs increased due to need for frequent updates across distributed devices, but gradient compression was found to mitigate this challenge. Focusing on the trade-offs between Federated and centralized learning systems, this research provides important hints for future studies on privacy preserving AI in cloud environments.