# Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet

arXiv: https://arxiv.org/abs/2201.03027 · HTML: https://mlsanity.com/paper/2201.03027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)

## Abstract (arXiv)

Identifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores meta-generalization for a multiparty privacy learning model in graynet to improve the performance of anomaly multimedia traffic identification. The multiparty privacy learning model in graynet is a globally shared model that is partitioned, distributed and trained by exchanging multiparty parameters updates with preserving private data. The meta-generalization refers to discovering the inherent attributes of a learning model to reduce its generalization error. In experiments, three meta-generalization principles are tested as follows. The generalization error of the multiparty privacy learning model in graynet is reduced by changing the dimension of byte-level imbedding. Following that, the error is reduced by adapting the depth for extracting packet-level features. Finally, the error is reduced by adjusting the size of support set for preprocessing traffic-level data. Experimental results demonstrate that the proposal outperforms the state-of-the-art learning models for identifying anomaly multimedia traffic.