Barycentric Coded Distributed Computing with Flexible Recovery Threshold for Collaborative Mobile Edge Computing

2025-09-11

AI Summary

In collaborative mobile edge computing (MEC), heterogeneous edge nodes cause straggler-induced delays, while existing coded distributed computing (CDC) schemes suffer from two critical limitations: fixed recovery thresholds and numerical instability due to poles in encoding/decoding functions, leading to inaccurate and unreliable decoding. To address these issues, this paper proposes a novel CDC framework based on barycentric rational interpolation. The method enables flexible recovery thresholds—i.e., exact reconstruction from any number of returned subtasks—eliminates poles to ensure numerical stability, provides tunable approximation accuracy via rational interpolation, and integrates gradient coding to accelerate distributed training. The framework operates seamlessly over both finite fields and the real number field. Experimental results demonstrate that, compared with state-of-the-art CDC approaches, the proposed method significantly reduces latency, improves approximation accuracy, enhances system robustness against stragglers and heterogeneity, and substantially boosts the efficiency of edge-coordinated computation.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Collaborative mobile edge computing (MEC) has emerged as a promising paradigm to enable low-capability edge nodes to cooperatively execute computation-intensive tasks. However, straggling edge nodes (stragglers) significantly degrade the performance of MEC systems by prolonging computation latency. While coded distributed computing (CDC) as an effective technique is widely adopted to mitigate straggler effects, existing CDC schemes exhibit two critical limitations: (i) They cannot successfully decode the final result unless the number of received results reaches a fixed recovery threshold, which seriously restricts their flexibility; (ii) They suffer from inherent poles in their encoding/decoding functions, leading to decoding inaccuracies and numerical instability in the computational results. To address these limitations, this paper proposes an approximated CDC scheme based on barycentric rational interpolation. The proposed CDC scheme offers several outstanding advantages. Firstly, it can decode the final result leveraging any returned results from workers. Secondly, it supports computations over both finite and real fields while ensuring numerical stability. Thirdly, its encoding/decoding functions are free of poles, which not only enhances approximation accuracy but also achieves flexible accuracy tuning. Fourthly, it integrates a novel BRI-based gradient coding algorithm accelerating the training process while providing robustness against stragglers. Finally, experimental results reveal that the proposed scheme is superior to existing CDC schemes in both waiting time and approximate accuracy.

Problem

• Mitigates straggler effects in mobile edge computing systems
• Overcomes fixed recovery threshold limitations in distributed computing
• Eliminates numerical instability and decoding inaccuracies in computations

Innovation

• Barycentric rational interpolation for flexible decoding
• Pole-free encoding/decoding ensuring numerical stability
• BRI-based gradient coding for straggler-resistant training