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Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing

arXiv 2312.12666 ↗First posted 19 Dec 2023cs.LG cs.CY cs.SIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 68 references that are in the map. First posted on arXiv 19 Dec 2023.

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25 of 68 references.

References · 25

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13 more
  1. 2020Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translationcited by 22
  2. 2022GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendationscited by 22
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