OPTIMIZING RESOURCE ALLOCATION IN DISTRIBUTED MACHINE LEARNING SYSTEMS: A HYBRID APPROACH COMBINING REINFORCEMENT LEARNING AND GAME THEORY - Butwall, Mani; Rajaan, Rajesh (COMPUSOFT: An International Journal of Advanced Computer Technology, 2023)
As machine learning models grow in complexity and scale, efficient resource allocation in distributed systems becomes a critical challenge. This paper introduces a novel hybrid approach combining Reinforcement Learning (RL) and Game Theory (GT) to optimize resource allocation in distributed machine learning systems. We propose a framework that leverages RL for dynamic resource management and GT for strategic decision-making among distributed nodes. The efficacy of our approach is evaluated through simulations and real-world benchmarks, demonstrating significant improvements in system performance and resource utilization.