| Title | Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data |
| Authors | Yang, Chengxu Wang, Qipeng Xu, Mengwei Chen, Zhenpeng Bian, Kaigui Liu, Yunxin Liu, Xuanzhe |
| Affiliation | Peking Univ, Key Lab High Confidence Software Technol, MoE, Beijing, Peoples R China Peking Univ, MoE, Key Lab High Confidence Software Technol, State Key Lab Networking & Switching Technol BUPT, Beijing, Peoples R China Peking Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Microsoft Res, Beijing, Peoples R China |
| Issue Date | 2021 |
| Publisher | PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021) |
| Abstract | 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, 2.32x 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. |
| URI | http://hdl.handle.net/20.500.11897/634208 |
| ISBN | 978-1-4503-8312-7 |
| DOI | 10.1145/3442381.3449851 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 高可信软件技术教育部重点实验室 其他实验室 信息科学技术学院 |
