Poster: Testbed in Wireless City Mesh Network with Application to Federated Learning Experiments

Abstract

The increase of the computing capacity of IoT devices and the appearance of lightweight machine learning frameworks have led to the situation that machine learning can nowadays be run in IoT applications at the network edge. There is an opportunity to implement machine learning algorithms with the more and more computationally powerful edge nodes and using the ever increasing amount of local data coming from nearby sensors. For this purpose, federated learning becomes a promising machine learning approach, where a machine learning model is trained by various nodes using their local data. For performing practical federated learning experiments, we have built a testbed deployed within a wireless city mesh network with geographically distributed low capacity devices. We describe the testbed implementation and show its potential to experimentally study federated learning protocols and algorithms in real edge environments.

Toward Smart Community Networks

“…Federated learning, a form of distributed ML, shows it is possible to efficiently utilize network resources, automating management for IoT users and the operating costs for cellular operators [14]. Its application to a distributed network has been tested for traffic classification or anomaly detection, but we need more evidence on its scalability and applicability in real cases [15].…”