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Strategic Federated Learning: A Novel Game-Theoretic Approach to Secure and Efficient IoD
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Author(s): Koppireddy Chandra Sekhar (Pragati Engineering College, India), D. V. D. Sri Varshini (Pragati Engineering College, India), S. Geetha Naga Sri Lakshmi (Pragati Engineering College, India)and Manas Kumar Yogi (Pragati Engineering College, India)
Copyright: 2026
Pages: 48
Source title:
Enhancing Surveillance With Blockchain and IoT Drone Technology
Source Author(s)/Editor(s): Shakir Khan (Imam Mohammad Ibn Saud Islamic University, Saudi Arabia), Hadeel Alsolai (Princess Nourah bint Abdulrahman University, Saudi Arabia), Arvind Panwar (Galgotias University, India)and Vishal Jain (School of Engineering and Technology, Vivekananda Institute of Professional Studies, New Delhi, India)
DOI: 10.4018/979-8-3373-4277-1.ch007
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Abstract
A smart, game-theoretic federated learning model has been produced by researchers to enhance IoD networks and their security and efficiency. In contrast to conventional centralized systems, which consume bandwidth, jeopardize privacy, and deplete 30-40 percent of battery of a drone, this solution uses drones in a way that are clever rationale agents. It promotes cooperation and discourages free-riding using Nash equilibrium and Stackelberg games. A combination of privacy protection, Byzantine-resilient aggregation, and on-demand resource allocation is present in the system to address the issues of data poisoning and inference attacks. It scales beautifully, tests demonstrate that it can be up to 25 percent faster to converge than other federated learning systems, and 20 percent better at resisting an attack. Using coalition-forming algorithms allows having drone collaboration remained stable, in spite of diverse or even diminished means. Gradient compression and hierarchical aggregation reduces the cost of communication at some marginal benefit in accuracy of the model.
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