Optimizing Federated Learning Efficiency for Real-Time Online Course Applications

The escalating demand for real-time interaction in mass online courses necessitates efficient and scalable data processing. This paper investigates the efficacy of an energy-efficient federated learning (FL) framework for enabling real-time functionalities within such educational platforms. By lever-aging decentralized computation across numerous student devices, the proposed framework aims to reduce server load and enhance responsiveness while minimizing energy consumption. This research evaluates the framework’s performance in tasks such as real-time question answering, immediate feedback provision, and dynamic content adaptation. Experimental results, conducted on simulated and real-world online course datasets, demonstrate the framework’s ability to maintain high accuracy and low latency for real-time features, while significantly improving energy efficiency compared to traditional centralized learning approaches. The findings highlight the potential of energy efficient FL frameworks to revolutionize the delivery of interactive and resource conscious online education.