Federated CNN-Transformer: Enabling Distributed Sensing-Assisted Beam Prediction in ISAC Systems for IoT Applications

Integrated Sensing and Communication (ISAC) technology provides robust support for the development of the Internet of Things (IoT) by leveraging its powerful sensing and communication capabilities. Sensing-assisted beam prediction techniques effectively enhance the communication quality and efficiency of ISAC systems, which is crucial for achieving high-speed and stable communication among IoT devices. However, ensuring high accuracy in beam prediction typically relies on traditional centralized architectures, which intrinsically pose risks to privacy and security when the central server is compromised. Therefore, this inherent trade-off between high prediction accuracy and data security poses significant challenges in privacy-sensitive IoT deployments. To resolve this fundamental contradiction, we propose a federated CNN-Transformer method for distributed sensing-assisted beam prediction in ISAC systems. Specifically, We propose an improved federated learning (FL) framework where clients transmit not only gradients but also nonlinear features to the server for subsequent computations through task offloading while enforcing client data privacy security. Addressing this problem is particularly challenging due to scattering and noise issues in the propagation of radar sensing signals. To address this, we design a hybrid model that integrates CNN and Transformer for the client side, which effectively captures both local and global features of the sensing signals, thereby improving prediction accuracy. Experimental results demonstrate that, compared to traditional centralized methods, our proposed method not only achieves significant improvements in prediction accuracy but also offers unique advantages in terms of data privacy and security, mitigating the risk of data leakage.