Hierarchical Federated Learning-enabled Communication-assisted Remote Sensing Target Recognition

Integrated Sensing and Communication (ISAC) is recognized as a pivotal technology for the upcoming 6G era. In ISAC, communication-assisted sensing enhances the communication capabilities of multiple sensor nodes and strengthens their sensing abilities, especially within the domain of remote sensing target recognition, which is of great significance for accurately identifying targets from massive remote sensing data. However, the explosive growth of model size and data volume has placed a heavy burden on communication links. Motivated by this, this paper proposes a communication-assisted remote sensing target recognition framework based on hierarchical federated learning (HFL). Specifically, after the edge nodes complete cluster aggregation, the fully connected layer parameters are sent back to the client, while the parameters of the convolutional layer are uploaded and aggregated in the cloud server, and then sent back to the cluster server, which in turn sends them to each client. The cloud server only aggregates the convolution layer parameters and distributes them to the client. To further reduce communication overhead, an adaptive sparsity method is introduced. By combining error compensation and Top-k amplitude pruning, a very high sparsity is achieved at the expense of minimal accuracy. Simulation results illustrate that the presented algorithm surpasses the benchmark scheme in both communication overhead and average test accuracy in the task of remote sensing target recognition.