Federated Reinforcement Learning for Decentralized Traffic Signal Control in Smart Cities

This research explores the application of federated reinforcement learning to optimize traffic flow in urban environments without centralizing sensitive traffic data. Our proposed framework enables individual intersections to learn optimal signal timings cooperatively, significantly reducing congestion and emissions across a city-scale simulation, offering a practical solution for smart city infrastructure.