Collaborative Federated Learning for 6G With a Deep Reinforcement Learning Based Controlling Mechanism: A DDoS Attack Detection Scenario

Abstract

Abstract

Offering intelligent services with ultra low latency and high reliability is one of the main objectives of 6G networks. Federated Learning (FL) is a solution to enhance the security of data and the accuracy, in comparison with local training of data in devices. The transmission cost in conventional FL is high. Performing FL using edge infrastructure is a solution. However, edge servers might not be available at every location or the communication with edge resources may prolong the learning process. This paper proposes a collaborative federated learning approach to provide intelligent services through collaboration of various learning levels including central cloud level, edge cloud level, and device level. Computational capabilities of neighbourhood devices are exploited to provide a fast recognition via 6G D2D communication. The learning is modeled as an optimization that performs trade-off between recognition accuracy and response time of recognition for devices. Considering the dynamicity in communication and computation status of the network/devices, a deep reinforcement learning method is proposed to decide about the collaboration of learning levels, and performing the appropriate trade-off. For a DDoS attack detection scenario, the evaluation results show improvement in the gained rewards, the attack detection accuracy, the response time of recognition, and the accumulation of accuracy and response time.