# Distributed Learning for Time-varying Networks: A Scalable Design

arXiv: https://arxiv.org/abs/2108.00231 · HTML: https://mlsanity.com/paper/2108.00231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Structure of Deep Neural Networks with a Priori Information in Wireless Tasks](https://mlsanity.com/paper/1910.13728/) (2019)
- [Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks](https://mlsanity.com/paper/2103.11542/) (2021)

## Built on by (most cited first, 1 of 1)

- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)

## Abstract (arXiv)

The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence". With data spread over the communication networks and computing capability enhanced on the devices, distributed learning becomes a hot topic in both industrial and academic communities. Many frameworks, such as federated learning and federated distillation, have been proposed. However, few of them takes good care of obstacles such as the time-varying topology resulted by the characteristics of wireless networks. In this paper, we propose a distributed learning framework based on a scalable deep neural network (DNN) design. By exploiting the permutation equivalence and invariance properties of the learning tasks, the DNNs with different scales for different clients can be built up based on two basic parameter sub-matrices. Further, model aggregation can also be conducted based on these two sub-matrices to improve the learning convergence and performance. Finally, simulation results verify the benefits of the proposed framework by compared with some baselines.