Incremental Semi-supervised Federated Learning for Health Inference via
  Mobile Sensing
Guimin Dong Lihua Cai Mingyue Tang Laura E. Barnes Mehdi Boukhechba
Mobile sensing appears as a promising solution for health inference problem
(e.g., influenza-like symptom recognition) by leveraging diverse smart sensors
to capture fine-grained information about human behaviors and ambient contexts.
Centralized training of machine learning models can place mobile users'
sensitive information under privacy risks due to data breach and
misexploitation. Federated Learning (FL) enables mobile devices to
collaboratively learn global models without the exposure of local private data.
However, there are challenges of on-device FL deployment using mobile sensing:
1) long-term and continuously collected mobile sensing data may exhibit domain
shifts as sensing objects (e.g. humans) have varying behaviors as a result of
internal and/or external stimulus; 2) model retraining using all available data
may increase computation and memory burden; and 3) the sparsity of annotated
crowd-sourced data causes supervised FL to lack robustness. In this work, we
propose FedMobile, an incremental semi-supervised federated learning algorithm,
to train models semi-supervisedly and incrementally in a decentralized online
fashion. We evaluate FedMobile using a real-world mobile sensing dataset for
influenza-like symptom recognition. Our empirical results show that
FedMobile-trained models achieve the best results in comparison to the selected
baseline methods.