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• W4386248948 abstract "Integrated sensing and communication (ISAC) has been an attractive solution to address the issues of spectrum shortage and hardware overhead through the coexistence of sensing and communication function. In ISAC, one of the most meaningful topics is communication-assisted sensing, namely, sensing-centered ISAC, where the sensing performance is enhanced by the improvement of communication efficiency between multiple sensing nodes. As a classic architecture of distributed sensing, federated learning (FL) based communication-assisted sensing is continuously discussed. Note that the huge number of parameters in deep neural network (DNN) applied to FL brings a heavy burden to the communication link between sensing node and central server, which even leads to the disconnecting of training clients. In this paper, a novel federated transfer learning (FTL) framework is proposed to address the above issue. More specifically, a task-general sub-model of DNN is pre-trained as feature extractor to accelerate learning and only the task-specific sub-model is aggregated to reduce the communication overhead in the framework. The performance of our proposed scheme is evaluated and the simulation results demonstrate that our algorithm outperforms the benchmark in terms of communication overhead and average test accuracy." @default.
• W4386248948 created "2023-08-30" @default.
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• W4386248948 date "2023-05-20" @default.
• W4386248948 modified "2023-09-27" @default.
• W4386248948 title "Efficient Communication-Assisted Sensing based on Federated Transfer Learning" @default.
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• W4386248948 doi "https://doi.org/10.1109/infocomwkshps57453.2023.10226028" @default.
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