FedAPT: Joint Adaptive Parameter Freezing and Resource Allocation for Communication-Efficient Federated Vehicular Networks

Telematics technology development offers vehicles a range of intelligent and convenient functions, including navigation and mapping services, intelligent driving assistance, and intelligent traffic management. However, since these functions deal with sensitive information like vehicle location and driving habits, it is crucial to address concerns regarding information security and privacy protection. Federated learning (FL) is highly suitable for addressing such problems due to its characteristics, in which a client does not need to share private data and upload model parameters to a parameter server via the network. This results in the establishment of a federated vehicle network (FVN). As a distributed paradigm, the efficiency of communication is crucial in federated learning as it impacts all aspects of the FVN. This paper introduces a parameter freezing algorithm based on historical information to reduce the data transferred between the client and the parameter server in each round of communication, thus minimizing the communication overhead of federated learning. Additionally, we propose using a particle swarm algorithm to allocate network bandwidth to each vehicle based on the packet sizes sent by each vehicle (i.e., the non-freezing parameters) to minimize the communication latency in each FL round. Furthermore, due to the high time complexity of the particle swarm algorithm, we employ it to generate training data for training a transformer model with fast response and sufficient accuracy, thereby accelerating the bandwidth allocation process. Through extensive experiments, we prove the feasibility of our approach and its efficiency in improving communication in federated learning.