On the Communication Latency of Wireless Decentralized Learning

Navid Naderializadeh

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arXiv · 2002.04069

On the Communication Latency of Wireless Decentralized Learning

Navid Naderializadeh

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Abstract

We consider a wireless network comprising $n$ nodes located within a circular area of radius $R$, which are participating in a decentralized learning algorithm to optimize a global objective function using their local datasets. To enable gradient exchanges across the network, we assume each node communicates only with a set of neighboring nodes, which are within a distance $R n^{-\beta}$ of itself, where $\beta\in(0,\frac{1}{2})$. We use tools from network information theory and random geometric graph theory to show that the communication delay for a single round of exchanging gradients on all the links throughout the network scales as $\mathcal{O}\left(\frac{n^{2-3\beta}}{\beta\log n}\right)$, increasing (at different rates) with both the number of nodes and the gradient exchange threshold distance.

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BibTeX RIS

Navid Naderializadeh. 2020-02-10. On the Communication Latency of Wireless Decentralized Learning. https://arxiv.org/abs/2002.04069

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