1 research outputs found
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
This paper presents the design, implementation, and evaluation of FLSys, a
mobile-cloud federated learning (FL) system that supports deep learning models
for mobile apps. FLSys is a key component toward creating an open ecosystem of
FL models and apps that use these models. FLSys is designed to work with mobile
sensing data collected on smart phones, balance model performance with resource
consumption on the phones, tolerate phone communication failures, and achieve
scalability in the cloud. In FLSys, different DL models with different FL
aggregation methods in the cloud can be trained and accessed concurrently by
different apps. Furthermore, FLSys provides a common API for third-party app
developers to train FL models. FLSys is implemented in Android and AWS cloud.
We co-designed FLSys with a human activity recognition (HAR) in the wild FL
model. HAR sensing data was collected in two areas from the phones of 100+
college students during a five-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 (non-IID)
data that affects FL model training in the wild. A sentiment analysis (SA)
model is used to demonstrate how FLSys effectively supports concurrent models,
and it uses a dataset with 46,000+ tweets from 436 users. We conducted
extensive experiments on Android phones and emulators showing that FLSys
achieves good model utility and practical system performance.Comment: The first two authors contributed equally to this wor