Federated Learning-Based Multi-Objective Optimization for IoT-Enabled Distributed Environmental Monitoring in Consumer Electronics

In this paper, the integration of the Internet of Things (IoT) in consumer electronics has significantly improved the ability to monitor environmental conditions in real time. This advancement is particularly crucial in patient care and medical data management. However, the vast volume of data generated by these devices demands advanced optimization algorithms to efficiently manage and evaluate this information. This research proposes a next-generation environmental monitoring system designed with a novel Federated Learning-Based Multi-Objective Optimization (FL-MOO) algorithm. This algorithm introduces specific learning parameters designed to enhance data processing quality, ensuring real-time monitoring with minimal latency and optimal resource allocation. By balancing the computational load and maintaining the accuracy of the data across devices enabled by distributed IoT, the proposed FL-MOO algorithm achieves superior performance in terms of speed, accuracy, and resource utilization compared to conventional methods. Simulation experiments demonstrate the efficiency of the proposed algorithm in optimizing data processing within a distributed environment, effectively managing the load distribution between devices. The algorithm also significantly reduced environmental overhead, achieving an average energy consumption of 0.35145, a latency of 0.17376, and a load balancing value of 0.43452. This work paves the way for more scalable and powerful IoT-based solutions in smart health, driving advances in healthcare delivery, patient outcomes, and the effective integration of consumer electronics in environmental monitoring systems.