  While the Internet of Things (IoT) can benefit from machine learning by
outsourcing model training on the cloud, user data exposure to an untrusted
cloud service provider can pose threat to user privacy. Recently, federated
learning is proposed as an approach for privacy-preserving machine learning
(PPML) for the IoT, while its practicability remains unclear. This work
presents the evaluation on the efficiency and privacy performance of a readily
available federated learning framework based on PySyft, a Python library for
distributed deep learning. It is observed that the training speed of the
framework is significantly slower than of the centralized approach due to
communication overhead. Meanwhile, the framework bears some vulnerability to
potential man-in-the-middle attacks at the network level. The report serves as
a starting point for PPML performance analysis and suggests the future
direction for PPML framework development.
