Enhancing Cloud Data Privacy Through Federated Learning: A Decentralized Approach to AI Model Training

The federated learning model on cloud platforms adjusts the training of the artificial intelligence models, shifting focus on data security while retaining the previously used formula. Traditional centralized approaches towards training AI models are insecure and unsafe for data and privacy because of the vulnerability of exposing data in a cloud setting. Federated learning helps to train the ML models with the assistance of numerous edge devices or servers without gaining access to data in a central server. The concept describing one of the promising ways to learn on big data without transmitting it and without disclosing the data themselves is called federated learning; the current paper aims to explain the principles and methodologies of federated learning. Based on the literature and reports on simulations, this study evaluates the applicability of federated learning to privacy preservation compared to the centralized approach. The conclusions indicate that the possibilities of the analytic revolution in distributed model training based on federated learning create an opportunity to preserve data ownership and guarantee model quality in cloud environments.