FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
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DOI：
10.1109/tmc.2022.3223578
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发表时间：
2021-11
影响因子：
7.9
通讯作者：
Han Hu;Xiaopeng Jiang;Vijaya Datta Mayyuri;An M. Chen;D. Shila;Adriaan Larmuseau;Ruoming Jin;C. Borcea;Nhathai Phan
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文献类型：
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作者：
Han Hu;Xiaopeng Jiang;Vijaya Datta Mayyuri;An M. Chen;D. Shila;Adriaan Larmuseau;Ruoming Jin;C. Borcea;Nhathai Phan
本文介绍了移动-云联合学习(FL)系统FLSys的设计、实现和评估，该系统可以作为FL模型和应用程序的开放生态系统的关键组件。FLSys的设计目的是在带有移动传感数据的智能手机上工作。它平衡模型性能和资源消耗，容忍通信故障，并实现可伸缩性。在FLSys中，不同的FL聚合方式的不同的DL模型可以被不同的应用并发训练和访问。此外，FLSys还为第三方应用开发者提供了先进的隐私保护机制和通用API来访问FL模型。FLSys采用模块化设计，在Android和AWS云中实现。我们共同设计了具有人类活动识别(HAR)模型的FLSys。在为期4个月的时间里，在野外收集了100多名大学生的HAR传感数据。我们实现了Har-Wild，这是一个为移动设备量身定做的CNN模型，具有数据增强机制，以缓解数据非独立和相同分布的问题。一个情感分析模型也被用来证明FLSys有效地支持并发模型。本文报告了我们使用模拟、Android/Linux模拟和Android手机进行广泛实验的经验和教训，这些实验证明了FLSys实现了良好的模型实用程序和实际系统性能。
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.