Efficient Transmission and Secure Sharing of Sensing data under Distributed ISAC Conditions.

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for large-scale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.