Resource allocation in wireless networks with federated learning: Network adaptability and learning acceleration

Abstract

Deep reinforcement learning can effectively address resource allocation in wireless networks. However, its learning speed may be slower in more complex networks and a new policy should be learned for a newly-arrived system due to a lack of network adaptability. To address these issues, we propose a federated learning framework for resource allocation in wireless networks with multiple systems. It accelerates the learning speed by aggregating the policy at each system into a central policy and ensures network adaptability by using the central policy. Through experiments, we demonstrate that our proposed framework achieves both learning acceleration and network adaptability.

Keywords

• Deep learning
• Federated learning
• Reinforcement learning
• Resource allocation
• Wireless networks