With the emergence of integrated sensing, communication, and computation (ISCC) in the upcoming 6G era, federated learning with ISCC (FL-ISCC), integrating sample collection, local training, and parameter exchange and aggregation, has garnered increasing interest for enhancing training efficiency. Currently, FL-ISCC primarily includes two algorithms: FedAVG-ISCC and FedSGD-ISCC. However, the theoretical understanding of the performance and advantages of these algorithms remains limited. To address this gap, we investigate a general FL-ISCC framework, implementing both FedAVG-ISCC and FedSGD-ISCC. We experimentally demonstrate the substantial potential of the ISCC framework in reducing latency and energy consumption in FL. Furthermore, we provide a theoretical analysis and comparison. The results reveal that:1) Both sample collection and communication errors negatively impact algorithm performance, highlighting the need for careful design to optimize FL-ISCC applications. 2) FedAVG-ISCC performs better than FedSGD-ISCC under IID data due to its advantage with multiple local updates. 3) FedSGD-ISCC is more robust than FedAVG-ISCC under non-IID data, where the multiple local updates in FedAVG-ISCC worsen performance as non-IID data increases. FedSGD-ISCC maintains performance levels similar to IID conditions. 4) FedSGD-ISCC is more resilient to communication errors than FedAVG-ISCC, which suffers from significant performance degradation as communication errors increase.Extensive simulations confirm the effectiveness of the FL-ISCC framework and validate our theoretical analysis.

翻译：随着集成感知、通信与计算（ISCC）在即将到来的6G时代的兴起，融合样本采集、本地训练以及参数交换与聚合的ISCC联邦学习（FL-ISCC）因能提升训练效率而受到日益广泛的关注。目前，FL-ISCC主要包含两种算法：FedAVG-ISCC与FedSGD-ISCC。然而，对这些算法的性能与优势的理论理解仍然有限。为填补这一空白，我们研究了一个通用的FL-ISCC框架，并实现了FedAVG-ISCC和FedSGD-ISCC两种算法。我们通过实验证明了ISCC框架在降低联邦学习延迟与能耗方面的巨大潜力。此外，我们提供了理论分析与比较。结果表明：1）样本采集误差与通信误差均对算法性能产生负面影响，这凸显了在优化FL-ISCC应用时需要精心设计。2）在独立同分布（IID）数据下，由于具有多次本地更新的优势，FedAVG-ISCC的表现优于FedSGD-ISCC。3）在非独立同分布（non-IID）数据下，FedSGD-ISCC比FedAVG-ISCC更具鲁棒性；其中，FedAVG-ISCC中的多次本地更新会随着非独立同分布数据程度的增加而恶化性能，而FedSGD-ISCC则能保持与IID条件下相近的性能水平。4）FedSGD-ISCC比FedAVG-ISCC对通信误差具有更强的耐受性；随着通信误差的增加，FedAVG-ISCC会出现显著的性能下降。大量仿真实验证实了FL-ISCC框架的有效性，并验证了我们的理论分析。