  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.
