# A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT

arXiv: https://arxiv.org/abs/2201.09483 · HTML: https://mlsanity.com/paper/2201.09483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [An Asynchronous Parallel Stochastic Coordinate Descent Algorithm](https://mlsanity.com/paper/1311.1873/) (2013)
- [Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://mlsanity.com/paper/2003.00645/) (2020)
- [On the Acceleration of Deep Learning Model Parallelism with Staleness](https://mlsanity.com/paper/1909.02625/) (2019)
- [Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism](https://mlsanity.com/paper/2010.08899/) (2020)
- [Asynchronous Coordinate Descent under More Realistic Assumptions](https://mlsanity.com/paper/1705.08494/) (2017)
- [Interpreting Deep Classifier by Visual Distillation of Dark Knowledge](https://mlsanity.com/paper/1803.04042/) (2018)
- [On Distributed Quantization for Classification](https://mlsanity.com/paper/1911.00216/) (2019)
- [Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation](https://mlsanity.com/paper/2106.00999/) (2021)

## Abstract (arXiv)

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.