Federated learning (FL) is a classic paradigm of 6G edge intelligence (EI), which alleviates privacy leaks and high communication pressure caused by traditional centralized data processing in the artificial intelligence of things (AIoT). The implementation of multimodal federated perception (MFP) services involves three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately relying on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is crucial to the MFP networks. To address the above issues, this paper investigates service-oriented resource management with integrated sensing, communication, and computing (ISCC). With the incentive mechanism of the MFP service market, the resources management problem is redefined as a social welfare maximization problem, where the idea of "expanding resources" and "reducing costs" is used to improve learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

翻译：联邦学习作为6G边缘智能的经典范式，可缓解人工智能物联网中传统集中式数据处理导致的隐私泄露与高通信压力。多模态联邦感知服务的实施涉及三个子过程，包括基于感知的多模态数据生成、基于通信的模型传输及基于计算的模型训练，最终依赖于时间、频率和算力等可用的底层多域物理资源。因此，如何合理协调感知、通信与计算之间的多域资源调度对多模态联邦感知网络至关重要。为解决上述问题，本文研究了面向服务的通感算一体化资源管理。通过引入多模态联邦感知服务市场的激励机制，将资源管理问题重新定义为社会福利最大化问题，利用"资源拓展"与"成本削减"理念提升学习性能增益并降低资源成本。实验结果验证了所提资源调度机制的有效性与鲁棒性。