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Federated Learning Frameworks for Energy-Efficient AI in Distributed Data Centres
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Author(s): S. Prabakeran (SRM Institute of Science and Technology, India), T. Sethukarasi (RMK Engineering College, India)and V. Indumathi (SRM Institute of Science and Technology, India)
Copyright: 2025
Pages: 28
Source title:
Energy Efficient Algorithms and Green Data Centers for Sustainable Computing
Source Author(s)/Editor(s): P.J. Beslin Pajila (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India), Belfin Robinson Vimala (University of North Carolina, USA), Y. Harold Robinson (Francis Xavier Engineering College, India)and C. Gopala Krishnan (GITAM University, India)
DOI: 10.4018/979-8-3373-0766-4.ch016
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Abstract
The rising energy demands of large data centers call for energy-efficient AI training methods. Federated Learning (FL), a decentralized paradigm, offers a solution by enabling model training across distributed devices without centralizing sensitive data. This review explores FL's integration with distributed data centers to achieve energy efficiency, analyzing methods like federated averaging and energy-aware protocols to minimize resource use. It highlights techniques such as model compression, quantization, and adaptive FL to reduce on-device computation while maintaining performance. Practical implementation is discussed through tools like TensorFlow Federated and PySyft, with case studies from healthcare, finance, and IoT showcasing cost reductions and sustainability. Future research directions include combining FL with edge computing and low-power AI hardware, emphasizing FL's potential for scalable, sustainable AI.
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