Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers

2025-10-26

AI Summary

To address the straggler problem caused by heterogeneous node delays in distributed learning, this paper proposes a gradient coding scheme based on individualized probabilistic modeling of node-specific delay distributions. Unlike conventional approaches that assume homogeneous nodes or rely on high redundancy, our method explicitly models the stochastic delay distribution of each worker. By leveraging Lagrangian duality and convex optimization, we derive closed-form optimal encoding and decoding coefficients that minimize residual error while guaranteeing unbiased gradient estimation. The scheme is theoretically grounded for λ-strongly convex and μ-smooth loss functions, significantly reducing both data redundancy and computational overhead. Experiments demonstrate that the proposed method accelerates model convergence, enhances robustness against stragglers, and improves overall training efficiency compared to state-of-the-art gradient coding techniques.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In this paper, we propose an optimally structured gradient coding scheme to mitigate the straggler problem in distributed learning. Conventional gradient coding methods often assume homogeneous straggler models or rely on excessive data replication, limiting performance in real-world heterogeneous systems. To address these limitations, we formulate an optimization problem minimizing residual error while ensuring unbiased gradient estimation by explicitly considering individual straggler probabilities. We derive closed-form solutions for optimal encoding and decoding coefficients via Lagrangian duality and convex optimization, and propose data allocation strategies that reduce both redundancy and computation load. We also analyze convergence behavior for $λ$-strongly convex and $μ$-smooth loss functions. Numerical results show that our approach significantly reduces the impact of stragglers and accelerates convergence compared to existing methods.

Problem

• Mitigating straggler effects in distributed learning systems
• Optimizing gradient coding for heterogeneous straggler probabilities
• Reducing redundancy while ensuring unbiased gradient estimation

Innovation

• Optimally structured gradient coding scheme
• Closed-form solutions via convex optimization
• Data allocation reducing redundancy and computation