Improving Response Time of Home IoT Services in Federated Learning

For intelligent home IoT services with sensors and machine learning, we need to upload IoT data to the cloud server which cannot share private data for training. A recent machine learning approach, called federated learning, keeps user data on the device in the distributed computing environment. Though federated learning is useful for protecting privacy, it experiences poor performance in terms of the end-to-end response time in home IoT services, because IoT devices are usually controlled by remote servers in the cloud. In addition, it is difficult to achieve the high accuracy of federated learning models due to insufficient data problems and model inversion attacks. In this paper, we propose a local IoT control method for a federated learning home service that recognizes the user behavior in the home network quickly and accurately. We present a federated learning client with transfer learning and differential privacy to solve data scarcity and data model inversion attack problems. From experiments, we show that the local control of home IoT devices for user authentication and control message transmission by the federated learning clients improves the response time to less than 1 second. Moreover, we demonstrate that federated learning with transfer learning achieves 97% of accuracy under 9,000 samples, which is only 2% of the difference from centralized learning.

FLHub: a Federated Learning model sharing service

As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated learning in the distributed computing framework is attracting attention. The central server does not collect sensitive and personal data from clients in federated learning, but it only aggregates the model parameters. Though federated learning helps protect privacy, it is difficult for machine learning developers to share the models that they could utilize for different-domain applications. In this paper, we propose a federated learning model sharing service named Federated Learning Hub (FLHub). Users can upload, download, and contribute the model developed by other developers similarly to GitHub. We demonstrate that a forked model can finish training faster than the existing model and that learning progressed more quickly for each federated round.