Federated learning is widely applied in mobile networks, utilizing a distributed approach for both local and global model training, effectively ensuring data security. However, its application in Internet of Vehicles faces three major challenges: frequent global communication causing high latency, limited computational and power resources of vehicles, and bandwidth fluctuations induced by highly dynamic network environments. These factors collectively hinder the execution of federated learning systems and significantly reduce efficiency. To address these issues, this study proposes an adaptive resource scheduling strategy integrating mobility and bandwidth prediction with multi-agent reinforcement learning. This strategy introduces a multidimensional prediction model combining wavelet neural network (WNN) and extended long short-term memory (xLSTM) to forecast vehicle mobility positions and bandwidth, providing accurate inputs for the multi-agent reinforcement learning framework. Concurrently, a multi-agent deep Q-network (MADQN) dynamic resource scheduling algorithm is employed to tackle resource allocation and power control challenges, thus mitigating the impact of the tail problem on system efficiency and enabling autonomous decision-making by edge nodes. Experimental results show that, compared to traditional methods, this approach significantly reduces system costs and energy consumption, while improving model performance and transmission success rates.