Federated Learning Meets Swarm Intelligence: A Privacy-Centric Framework for Big Data Processing

Information processing solutions that offer both intelligence and security are crucial to handle the constantly increasing big data in applications including healthcare alongside smart cities and IoT networks. A new framework blends Federated Learning (FL) and Swarm Intelligence (SI) to tackle three main barriers which affect privacy retention and scalability and real-time processing in big data analytics. Federated Learning authorizes multiple devices to perform model training independently without showing bare data thus protecting privacy and adhering to data regulations. Resource management in dynamic environments becomes efficient through the use of Swarm Intelligence techniques which include Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). These methods optimize learning parameters and enhance model convergence. The proposed framework provides substantial enhancements for calculation speed together with model precision and adapts well to different data types in conditions marked by reduced bandwidth capabilities and stringent privacy management needs. The hybrid approach proves successful through experimental validations which occur across healthcare analytics, smart grid monitoring and IoT-based anomaly detection applications. The established groundwork will enable the creation of upcoming big data systems that are privacy-aware, smart and flexible in scale.