Real-Time Cloud-Native Processing Techniques for Enhancing Federated Learning in Distributed Applications

Federated Learning (FL) has become a new paradigm in the privacy-preserving domain for training machine learning models over the distributed clients without transferring raw data. But, the real FL offer challenges for dynamic and heterogeneous environments with the real-time data processing due to the reason of the environments being heterogeneous and dynamic. This paper offers a new design of a cloud-native architecture to overcome these barriers through scalable and efficient real-time data processing. The architecture combines microservices, container orchestration, and streaming frameworks for low-latency data and adaptive resource management. Implementing this framework in distributed cloud environments enables us to improve model convergence speed, system scalability, and performance in different federated learning scenarios. The experiments performed on benchmark datasets prove the approach to be better in terms of communication responsiveness and system responsiveness. This work provides a flexible and robust solution for deploying federated learning in constrained real-time, large-scale, resource-limited applications.