Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
| Autor: | Yunxin Liu, Chengxu Yang, Zhenpeng Chen, Xuanzhe Liu, Kaigui Bian, Qipeng Wang, Mengwei Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Process (engineering) Scale (chemistry) Training time 020206 networking & telecommunications Machine Learning (stat.ML) 02 engineering and technology Data science Federated learning Machine Learning (cs.LG) Upload Empirical research Computer Science - Distributed Parallel and Cluster Computing Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Distributed Parallel and Cluster Computing (cs.DC) Device failure Protocol (object-oriented programming) |
| Zdroj: | WWW |
| Popis: | Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature. In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. We collect large-scale data from 136k smartphones that can faithfully reflect heterogeneity in real-world settings. We also build a heterogeneity-aware FL platform that complies with the standard FL protocol but with heterogeneity in consideration. Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings. Results show that heterogeneity causes non-trivial performance degradation in FL, including up to 9.2% accuracy drop, 2.32 × lengthened training time, and undermined fairness. Furthermore, we analyze potential impact factors and find that device failure and participant bias are two potential factors for performance degradation. Our study provides insightful implications for FL practitioners. On the one hand, our findings suggest that FL algorithm designers consider necessary heterogeneity during the evaluation. On the other hand, our findings urge system providers to design specific mechanisms to mitigate the impacts of heterogeneity. |
| Databáze: | OpenAIRE |
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