首个针对边缘设备约束的异构模型联邦学习评测平台
PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
- 构建首个面向边缘设备资源约束的异构联邦学习评测平台
- 在多种数据任务和指标下验证算法适用性与异构模式
- 揭示现有算法在真实边缘场景下的性能表现差异
近年来，基于多样化资源约束的边缘设备进行异构模型联邦学习已成为显著趋势。相较于传统联邦学习中假设所有设备使用相同模型架构的做法，异构模型联邦学习更具实用性与灵活性，可按部署需求定制模型。然而，此前研究从未在实际边缘设备约束下深入评估异构模型联邦学习算法，并对不同数据场景和指标进行量化分析，这促使我们重新思考并重新评估该范式。本文构建首个系统平台 PracMHBench，用于在真实边缘设备约束下评估异构模型联邦学习，对多种模型异构算法进行分类并测试其在多个数据任务与指标上的表现。基于该平台，我们在不同边缘约束条件下对算法进行大量实验，观察其适用性及对应的异构模式。
原文摘要 · Abstract (English)
Federating heterogeneous models on edge devices with diverse resource constraints has been a notable trend in recent years. Compared to traditional federated learning (FL) that assumes an identical model architecture to cooperate, model-heterogeneous FL is more practical and flexible since the model can be customized to satisfy the deployment requirement. Unfortunately, no prior work ever dives into the existing model-heterogeneous FL algorithms under the practical edge device constraints and provides quantitative analysis on various data scenarios and metrics, which motivates us to rethink and re-evaluate this paradigm. In our work, we construct the first system platform \textbf{PracMHBench} to evaluate model-heterogeneous FL on practical constraints of edge devices, where diverse model heterogeneity algorithms are classified and tested on multiple data tasks and metrics. Based on the platform, we perform extensive experiments on these algorithms under the different edge constraints to observe their applicability and the corresponding heterogeneity pattern.
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