FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

By Yiqiang Chen, Jindong Wang, Chaohui Yu, Wen Gao, Xin Qin. 2019

933 citations recorded by the source corpus.

With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning. Wearable activity recognition experiments and real Parkinson's disease auxiliary diagnosis application have evaluated that FedHealth is able to achieve accurate and personalized healthcare without compromising privacy and security. FedHealth is general and extensible in many healthcare applications.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science

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Citations in the corpus

FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], ﬁnance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110, 111].”
Federated Learning for Smart Healthcare: A Survey (2021, background): “• FL is also useful for facilitating in-home health monitoring, by training a global model from distributed homes under the control of a data server, while preventing data leakage by keeping user data locally [92], [93].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “BothHFLandVFLmaybeineffectivewhen the overlap of data sample space and feature space is marginal for the clients, e.g., in wearable healthcare [56].”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “[39] explores transfer learning to improve the accuracy of global models trained to analyze IoHT data.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “WHO RA1 [61, 196, 228] [26, 137, 218] [157, 169, 198] [144, 164, 199] [63, 70, 193] [16, 174, 191] [78, 110, 228] [52, 81, 216] [32, 56, 222] [30, 127, 173] RA2 [114, 175, 187] [215, 221, 224] [29, 111, 113] [13, 199] [3, 10, 40] [109, 155, 168] [45, 172, 193] [20, 52, 57] [56, 165, 197] [173, 199, ”
Towards Personalized Federated Learning (2021, background): “For example, in health monitoring applications, the distributions of users’ activity data vary considerably according to their habits and lifestyle patterns [24, 43].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “[120] Healthcare cooperation FTL CNN Hospitals Cloud server A scheme for federated activity recognition with FL.”
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “A federated transfer learning approach for wearable healthcare devices is proposed in [67].”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, methodology): “In personalized monitoring, physical activity classification task was performed with FedHealth by Y. Chen et al. (2020).”
Self-supervised Cross-silo Federated Neural Architecture Search (2021, background): “[1], [8]; 2) Vertical Federated Learning (VFL): VFL describes the scenarios where different organizations with a common set of users train a cooperative model to better utilize the data with distributed features [2], [9]–[12]; 3) Federated Transfer Learning (FTL): FTL applies to the cases where the ”
A Survey on federated learning* (2020, methodology): “For a real application, Chen et al. constructed a FedHealth model that gather data owned by different organizations and offer personalized service for healthcare through federated transfer learning [19].”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, methodology): “Chen et al. propose a federated transfer learning framework for personalized healthcare [11].”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, background): “Care (HC) [10], [11] and Human Activity Recognition (HAR) [12], [13] generate a substantial amount of multivariate time-series data.”
FedBABU: Towards Enhanced Representation for Federated Image Classification (2021, background): “In addition, [43, 7] utilized transfer learning to enhance local models by transferring knowledge between related clients.”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “FedHealth performs data aggregation through FL and then builds personalized models through transfer learning.”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, methodology): “For example, using a human-activity-recognition dataset, a global model (FedHealth) was pre-trained using 80% of the data before deploying it to be locally trained and then aggregated 99 .”