Federated Learning for Privacy-Preserving Big Data Analytics in Cloud Environments

The rapid increase of big data in cloud systems unlocks plentiful intelligent analysis chances but it needs immediate attention for data privacy protection and security management alongside regulatory adherence. The centralized educational approach forces sensitive information to gather at a single station thus creating potential risks of data theft and privacy violations. This paper evaluates Federated Learning (FL) because it functions as a decentralized privacy-preserving approach that trains models collectively based on distributed data without sharing actual data values. Our framework stands as a novel FL design meant for heterogeneous cloud systems where it implements secure aggregation approaches with adaptive client pick and differential privacy functionalities to deliver strong security while building scalability. The proposed framework demonstrates superior or equal predictive abilities to centralized models through extensive real-world large-scale dataset experiments while upholding rigorous privacy conditions. The research confirms Federated Learning functions as a workable solution for protecting big data analytics across compliant and large-scale cloud environments.