A Hybrid Aggregation Approach for Federated Learning to Improve Energy Consumption in Smart Buildings

As the world’s economy and urbanization develop rapidly, energy shortages and pollution are becoming major challenges. In general, buildings are responsible for approximately 40% of global energy consumption. To combat this challenge, the adoption of intelligent buildings is strongly recommended. Therefore, through the data collected by the smart buildings, effective solutions should be incorporated to limit their impact on the environment; Machine learning (ML) has proved great success in sensor-based Energy consumption. Trapping in 10-cal optima and slow convergence are the main difficulties of the Backpropagation (BP) learning algorithm. This has an impact on the neural network’s performance. To recover the drawback, this paper proposes a hybrid protocol FedLM-PSO that combines Particle Swarm optimization (PSO) and Levenberg Marquardt (LM) to train MLP models in a Federated Learning environment to find the near-optimal configurations for FL. In addition, FedLM-PSO evolves the way clients upload data to servers and reduces the amount of data sent, which enhances bandwidth consumption. According to the results, the FedLM-PSO is more accurate and requires fewer rounds of communication than FedAVG.