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

arXiv: https://arxiv.org/abs/2312.12666 · HTML: https://mlsanity.com/paper/2312.12666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 68 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
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- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
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- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Federated Class-Incremental Learning](https://mlsanity.com/paper/2203.11473/) (2022)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [A distillation-based approach integrating continual learning and federated learning for pervasive services](https://mlsanity.com/paper/2109.04197/) (2021)
- [Multimodal Federated Learning on IoT Data](https://mlsanity.com/paper/2109.04833/) (2021)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation](https://mlsanity.com/paper/2007.07867/) (2020)
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- [On Estimating Recommendation Evaluation Metrics under Sampling](https://mlsanity.com/paper/2103.01474/) (2021)
- [From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques](https://mlsanity.com/paper/2107.00948/) (2021)
- [Hardware Memory Management for Future Mobile Hybrid Memory Systems](https://mlsanity.com/paper/2004.05518/) (2020)
- [Perigee: Efficient Peer-to-Peer Network Design for Blockchains](https://mlsanity.com/paper/2006.14186/) (2020)
- [Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2303.13782/) (2023)
- [Federated learning with incremental clustering for heterogeneous data](https://mlsanity.com/paper/2206.08752/) (2022)
- [Efficient Multitask Learning on Resource-Constrained Systems](https://mlsanity.com/paper/2302.13155/) (2023)
- [FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis](https://mlsanity.com/paper/2302.11823/) (2023)
- [FPGA-based Hyrbid Memory Emulation System](https://mlsanity.com/paper/2011.04567/) (2020)
- [Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning](https://mlsanity.com/paper/2112.15530/) (2021)
- [SmartON: Just-in-Time Active Event Detection on Energy Harvesting Systems](https://mlsanity.com/paper/2103.00749/) (2021)

## 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.