Device to Device

Device-to-device (D2D) communication focuses on enabling direct data exchange between nearby wireless devices, bypassing traditional base stations to improve efficiency and reduce latency. Current research emphasizes optimizing D2D for machine learning tasks, particularly federated learning, using techniques like reinforcement learning to manage spectrum allocation, power control, and data transfer strategies within dynamic network topologies. This work is significant for enhancing the performance and scalability of distributed applications, such as anomaly detection in IoT networks and improving the efficiency of collaborative machine learning in resource-constrained environments.