Smart Battery Management System for Mobile Consumer Electronics Based on Federal Split Learning Model

Mobile consumer devices have become essential components of modern society, but their reliance on rechargeable batteries creates issues with overcharging, overdischarging, and battery health. The demand for effective and privacy-aware battery management becomes even more obvious in view of the fast changing environment of Smart IoT devices. Reliable power supplies and predicted battery performance are essential for the seamless integration of these smart IoT devices into daily life. This research offers a novel recurrent neural network (RNN) based training method for battery management systems in mobile consumer electronics and smart IoT devices. This approach addresses the heterogeneous ecosystem of smart IoT devices by allowing model training across decentralized data sources while protecting user data privacy by Federated Split Learning (FSL). Sharing extensive model parameters from these devices is restricted by FSL’s emphasis on privacy. To overcome this, it is suggested to break the RNN model into smaller sub-networks, each of which would be trained using data segments from various consumer electronics products and intelligent IoT sensors. Following their transmission to a federated server for aggregation, these sub-networks help advance mobile technology, consumer electronics, and the larger Smart IoT ecosystem by producing a more reliable RNN model for battery performance prediction.