Zone-based Federated Learning for Mobile Sensing Data
Zone-based Federated Learning for Mobile Sensing Data
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DOI：
10.1109/percom56429.2023.10099308
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Xiaopeng Jiang;Thinh On;Nhathai Phan;Hessamaldin Mohammadi;Vijaya Datta Mayyuri;An M. Chen;Ruoming Jin;C. Borcea
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文献类型：
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作者：
Xiaopeng Jiang;Thinh On;Nhathai Phan;Hessamaldin Mohammadi;Vijaya Datta Mayyuri;An M. Chen;Ruoming Jin;C. Borcea
提出了一种基于区域的联合学习算法(ZoneFL)，该算法在适应用户移动行为的同时，实现了良好的模型精度，并能随用户数量的增加而扩展，保护用户的数据隐私。ZoneFL将物理空间划分为地理区域，映射到移动-边缘-云系统架构，以实现良好的模型准确性和可扩展性。每个区域都有一个称为区域模型的联合训练模型，该模型很好地适应了该区域中用户的数据和行为。得益于FL的设计，在ZoneFL培训期间保护了用户的数据隐私。我们提出了两种新的基于区域的联合训练算法来优化区域模型以适应用户的移动行为：区域合并和分割(ZMS)和区域梯度扩散(ZGD)。ZMS通过合并相邻区域或将大区域拆分成较小的区域来调整区域地理分区，从而优化区域模型。与ZMS不同的是，ZGD保持了固定的区域，并通过结合来自相邻区域数据的梯度来优化区域模型。ZGD使用自我注意机制来动态控制一个区域对其邻居的影响。大量的分析和实验结果表明，ZoneFL在心率预测和人体活动识别两个模型上都明显优于传统的FL。此外，我们使用Android手机和AWS云开发了ZoneFL系统。该系统在63名用户的4个月的心率预测现场研究中使用，证明了ZoneFL在现实生活中的可行性。
This paper proposes Zone-based Federated Learning (ZoneFL) to simultaneously achieve good model accuracy while adapting to user mobility behavior, scaling well as the number of users increases, and protecting user data privacy. ZoneFL divides the physical space into geographical zones mapped to a mobile-edge-cloud system architecture for good model accuracy and scalability. Each zone has a federated training model, called a zone model, which adapts well to data and behaviors of users in that zone. Benefiting from the FL design, the user data privacy is protected during the ZoneFL training. We propose two novel zone-based federated training algorithms to optimize zone models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical partitions through merging of neighboring zones or splitting of large zones into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes a zone model by incorporating the gradients derived from neighboring zones' data. ZGD uses a self-attention mechanism to dynamically control the impact of one zone on its neighbors. Extensive analysis and experimental results demonstrate that ZoneFL significantly outperforms traditional FL in two models for heart rate prediction and human activity recognition. In addition, we developed a ZoneFL system using Android phones and AWS cloud. The system was used in a heart rate prediction field study with 63 users for 4 months, which demonstrated the feasibility of ZoneFL in real-life.