Federated Learning for Real-Time Demand Response by Data Centers Toward Energy Efficiency and Privacy Preservation - Mohannad Alhazmi; Alexis Pengfei Zhao; Wanzi Li; Chenlu Yang (IEEE Access, 2025)
Data centers are crucial in the era of digital economy and artificial intelligence. While enabling essential services in healthcare, business, and telecommunications, they consume substantial energy mostly from non-renewable sources and have enormous, environmental impacts. These in turn call for effective energy management strategies. This study presents a federated learning approach tailored for IoT-enabled data centers to optimize energy usage and enhance data privacy. Our novel method utilizes real-time data to generate effective and efficient actions in response to operational demands, in compliance with energy conservation and stringent privacy standards. We evaluate various demand response programs by the proposed method in different scenarios and case studies to demonstrate their efficacy in adapting to energy consumption patterns for optimal energy usage and cost reduction. The results show that by increasing local renewable energy capacity by 50%, energy costs are lowered by 24% and carbon emissions are reduced by 17%. This study reveals the significant environmental benefits and the crucial role of federated learning for sustainable energy management practices in data centers, while also ensuring high levels of data privacy and network traffic optimization for congestion prevention. Our method is particularly effective for preserving data confidentiality under strict privacy conditions, thus highlighting the value of federated learning as a transformative tool for managing energy in data centers.