Enhancing cooperative multi-agent reinforcement learning through the integration of R-STDP and federated learning

This paper introduces a novel approach to enhance the stability and efficiency of R-STDP in the context of federated learning. The primary objective is to stabilize the unbounded growth of R-STDP and make it more responsive to real-time changes. The methodology involves integrating R-STDP with Spiking Neural Networks and employing the norm of the neural network model for adjusting weighted aggregation in federated learning systems. The proposed method incorporates a mechanism where weights decay over time, depending on the duration since the agent last published its model. Additionally, the sampling time is dynamically adjusted based on the Euclidean norm, which measures the distance between the weight matrices of the agents and the server. The results demonstrate that the proposed event-triggered federated learning method significantly enhances learning speed and performance. At the same time, the dynamic aggregation interval efficiently reduces communication between the agents and the central server, especially after model convergence. This research presents a significant advancement in federated learning and offers a more stable, responsive, and efficient learning process.