Optimizing 6G Network Slice Mobility with Federated Deep Reinforcement Learning

Network slices support different services in mobile networks. They must ensure service availability and continuity. However, user mobility and changing demands cause issues. If a network slice does not move properly, service quality degrades. This issue is known as Network Slice Mobility (NSM). Existing research has some gaps. Some studies do not detect triggers before NSM decisions. Others do not predict future system states to improve mobility. Deep reinforcement learning (DRL) faces issues with incomplete observations. These challenges reduce NSM performance. This paper proposes a new solution. It introduces a prediction-based federated deep reinforcement learning (FDRL) framework. The goal is to improve network slice migration. The system periodically moves network slices. It also predicts future system conditions to support better decisions. The NSM problem is modeled as a Markov decision process (MDP). The proposed method solves it using a federated DRL approach. This framework includes two learning models. One model predicts future network conditions. The other model makes NSM decisions using DRL. Both models use federated learning. This reduces communication overhead. It also protects user privacy by keeping data decentralized. The proposed method is tested through experiments. The simulation results show significant improvements. The method outperforms existing solutions in several ways. First, it improves long-term network profit. Second, it reduces communication overhead. Third, it minimizes transmission time. These results prove the effectiveness of the approach. In conclusion, this paper introduces an advanced NSM framework. It predicts future system changes to improve decision-making. Federated learning ensures security and efficiency. The results prove that the method is effective and practical. This research makes an important contribution to network slice mobility in 6G networks.