# Toward Understanding the Impact of Staleness in Distributed Machine Learning

arXiv: https://arxiv.org/abs/1810.03264 · HTML: https://mlsanity.com/paper/1810.03264/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 29 of 29)

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- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data](https://mlsanity.com/paper/2203.01214/) (2022)
- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
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- [Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying](https://mlsanity.com/paper/2206.04742/) (2022)
- [ZenFlow: Enabling Stall-Free Offloading for LLM Training](https://mlsanity.com/paper/2505.12242/) (2025)
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- [DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning](https://mlsanity.com/paper/2406.02744/) (2024)
- [Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent](https://mlsanity.com/paper/2001.05918/) (2020)
- [Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers](https://mlsanity.com/paper/2102.06280/) (2021)
- [Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running](https://mlsanity.com/paper/2204.13878/) (2022)
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- [Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)](https://mlsanity.com/paper/2201.00626/) (2021)
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- [AFAFed -- Protocol analysis](https://mlsanity.com/paper/2206.14927/) (2022)
- [Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning](https://mlsanity.com/paper/2311.02130/) (2023)
- [Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks](https://mlsanity.com/paper/2501.01584/) (2025)
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- [MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments](https://mlsanity.com/paper/2503.01324/) (2025)

## Abstract (arXiv)

Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is challenging to directly monitor or control staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of stochastic gradient descent in non-convex optimization under staleness, matching the best-known convergence rate of O(1/√T).