# On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

arXiv: https://arxiv.org/abs/2106.09831 · HTML: https://mlsanity.com/paper/2106.09831/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019)
- [A theory of sequence indexing and working memory in recurrent neural networks](https://mlsanity.com/paper/1803.00412/) (2018)
- [Variable Binding for Sparse Distributed Representations: Theory and Applications](https://mlsanity.com/paper/2009.06734/) (2020)
- [Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks](https://mlsanity.com/paper/1909.09153/) (2019)
- [End to End Binarized Neural Networks for Text Classification](https://mlsanity.com/paper/2010.05223/) (2020)
- [Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives](https://mlsanity.com/paper/1705.03934/) (2017)
- [Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware](https://mlsanity.com/paper/1706.00280/) (2017)
- [HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics](https://mlsanity.com/paper/2003.01821/) (2020)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems](https://mlsanity.com/paper/2501.16863/) (2025)

## Abstract (arXiv)

A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary training ones. The strong revitalization of randomized algorithms can be framed in this prospect steering. We recently proposed a model for distributed classification based on randomized neural networks and hyperdimensional computing, which takes into account cost of information exchange between agents using compression. The use of compression is important as it addresses the issues related to the communication bottleneck, however, the original approach is rigid in the way the compression is used. Therefore, in this work, we propose a more flexible approach to compression and compare it to conventional compression algorithms, dimensionality reduction, and quantization techniques.