  In order to improve mobile data transparency, a number of network-based
approaches have been proposed to inspect packets generated by mobile devices
and detect personally identifiable information (PII), ad requests, or other
activities. State-of-the-art approaches train classifiers based on features
extracted from HTTP packets. So far, these classifiers have only been trained
in a centralized way, where mobile users label and upload their packet logs to
a central server, which then trains a global classifier and shares it with the
users to apply on their devices. However, packet logs used as training data may
contain sensitive information that users may not want to share/upload. In this
paper, we apply, for the first time, a Federated Learning approach to mobile
packet classification, which allows mobile devices to collaborate and train a
global model, without sharing raw training data. Methodological challenges we
address in this context include: model and feature selection, and tuning the
Federated Learning parameters. We apply our framework to two different packet
classification tasks (i.e., to predict PII exposure or ad requests in HTTP
packets) and we demonstrate its effectiveness in terms of classification
performance, communication and computation cost, using three real-world
datasets.
