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:产生巨大数据和最先进的机器学习技术的IoT装置将共同产生巨大的数据和最先进的机器学习技术,使网络系统发生革命性的变化。在许多不同的领域,从自主驱动到增强现实,分布式IoT装置计算特定目标功能,没有障碍检测、物体识别等简单形式。传统的云基方法侧重于将数据转移到一个中心地点,要么用于培训或推断,给网络资源带来巨大的压力。为了解决这个问题,我们根据我们的知识,开发了第一个机学习框架,用于分配高山多存取频道(GMAC)和或全方位AWGN频道的功能压缩。由于Kolmogorov-Arnold 代表理论,我们的机器学习框架可以通过设计,为IoT中所需的功能压缩任务任意计算任何功能。重要的是,原始传感器数据从未转移到一个中心点,用于培训或推断,从而减少通信。关于这些算法,我们提供了理论趋同保证和基于通信的上限。我们的模拟显示,为功能压缩而学习的编码和分解器比传统方法要好得多,我们机器学习到频道状况的频率变变的频率。