IoT devices generating enormous data and state-of-the-art machine learning techniques together will revolutionize cyber-physical systems. In many diverse fields, from autonomous driving to augmented reality, distributed IoT devices compute specific target functions without simple forms like obstacle detection, object recognition, etc. Traditional cloud-based methods that focus on transferring data to a central location either for training or inference place enormous strain on network resources. To address this, we develop, to the best of our knowledge, the first machine learning framework for distributed functional compression over both the Gaussian Multiple Access Channel (GMAC) and orthogonal AWGN channels. Due to the Kolmogorov-Arnold representation theorem, our machine learning framework can, by design, compute any arbitrary function for the desired functional compression task in IoT. Importantly the raw sensory data are never transferred to a central node for training or inference, thus reducing communication. For these algorithms, we provide theoretical convergence guarantees and upper bounds on communication. Our simulations show that the learned encoders and decoders for functional compression perform significantly better than traditional approaches, are robust to channel condition changes and sensor outages. Compared to the cloud-based scenario, our algorithms reduce channel use by two orders of magnitude.

翻译：物联网设备产生海量数据，同时最先进的机器学习技术将共同彻底改变信息物理系统。在从自动驾驶到增强现实等众多不同领域中，分布式物联网设备需计算特定目标函数，例如障碍检测、目标识别等简单形式之外的任务。传统的基于云的方法侧重于将数据传输到中央节点进行训练或推理，这对网络资源造成了巨大压力。为解决这一问题，我们首次提出了一个分布式功能压缩机器学习框架，该框架可同时适用于高斯多址接入信道（GMAC）和正交加性高斯白噪声信道。基于Kolmogorov-Arnold表示定理，我们的机器学习框架能够任意计算物联网中所需功能压缩任务的函数。重要的是，原始传感数据从未被传输到中央节点进行训练或推理，从而减少了通信开销。针对这些算法，我们提供了理论收敛性保证及通信上界。仿真结果表明，所学的功能压缩编码器和解码器性能显著优于传统方法，且对信道条件变化和传感器故障具有鲁棒性。与基于云的方案相比，我们的算法将信道使用量降低了两个数量级。