Federated Learning Approaches for Privacy-Preserving Artificial Intelligence in Distributed Cloud Environments

Recent growth of artificial intelligence (AI) in cloud computing has accelerated the need to address data privacy, security and regulatory compliance because standard centralized training paradigm enforced to date necessitate sensitive business data to be centralized at a single point. These architectures also face organizations with the increased risks of data in leakage, unauthorized access, and inference attacks, especially in multi-tenant and geographically distributed cloud infrastructure. To overcome these issues, federated learning (FL) has become an encouraging paradigm of decentralized learning, which allows the training of models without leaving raw data out of the local sources. The paper explores federated learning solutions to privacy saving AI on distributed clouds with its focus on architectural design, privacy-enhancing techniques, and optimizations at the system level. We suggest a cloud-native federated learning system that combines secure aggregation systems, differentiation privacy systems and adaptive communication techniques to trade privacy, model precision and scale. Throughout an intensive examination, it is shown that the suggested strategy would greatly r