Federated Learning in Cloud Computing: A Novel Approach to Decentralized Data Processing and Privacy Preservation

Abstract

Federated Learning (FL) is a decentralized machine learning platform that allows for cooperative model training across several devices while maintaining data privacy and security. This paper explores the integration of FL into cloud computing environments, offering a novel solution to data centralization issues that raise privacy concerns. Cloud computing, with its centralized data processing model, often exposes sensitive information to potential breaches. In comparison, federated learning allows for the creation of machine learning models on decentralized devices without having to transfer sensitive data to central servers, thus ensuring better privacy preservation. The study outlines the proposed framework of FL within cloud systems, focusing on maintaining data confidentiality and optimizing computational efficiency. We assess key algorithms like Federated Averaging (FedAvg) and their performance in cloud-based scenarios. Experimental results demonstrate that FL can reduce communication overhead, achieve comparable model accuracy, and effectively enhance privacy in distributed settings. By evaluating multiple client configurations and using datasets like MNIST and CIFAR-10,The findings suggest that Federated Learning maintains privacy while facilitating scalable, efficient, and decentralized data processing within cloud settings. This study adds to the expanding knowledge surrounding Federated Learning, highlighting its potential for wide-scale deployment in industries where data privacy is paramount, such as healthcare, finance, and smart infrastructure. Additionally, the study explores the future possibilities for improving FL algorithms, considering advancements in edge computing, federated transfer learning, and adaptive learning models.