# Decentralized Finite-Sum Optimization over Time-Varying Networks

arXiv: https://arxiv.org/abs/2402.02490 · HTML: https://mlsanity.com/paper/2402.02490/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2024.

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

- [Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling](https://mlsanity.com/paper/2502.14648/) (2025)
- [Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks](https://mlsanity.com/paper/2405.18031/) (2024)

## Abstract (arXiv)

We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently solved using variance reduction techniques. Our aim is to explore the lower complexity bounds (for communication and number of stochastic oracle calls) and find optimal algorithms. The paper studies strongly convex and nonconvex scenarios. To the best of our knowledge, variance reduced schemes and lower bounds for time-varying graphs have not been studied in the literature. For nonconvex objectives, we obtain lower bounds and develop an optimal method GT-PAGE. For strongly convex objectives, we propose the first decentralized time-varying variance-reduction method ADOM+VR and establish lower bound in this scenario, highlighting the open question of matching the algorithms complexity and lower bounds even in static network case.