FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

By Qiong Wu, Xu Chen, Zhi Zhou, Junshan Zhang. IEEE Transactions on Mobile Computing, 2020

391 citations recorded by the source corpus.

In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user’s monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user’s personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/f030656c4441093852ca0ad0d533b156a7c8a884

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Han Yu
2. Qiang Yang
3. Li-zhen Cui
4. Alysa Ziying Tan
5. Arne Küderle
6. Rodolfo Stoffel Antunes
7. Cristiano André da Costa
8. B. Eskofier

In Computer Vision and Pattern Recognition

1. Li-zhen Cui
2. Qiang Yang
3. B. Eskofier
4. Alysa Ziying Tan
5. Han Yu
6. Gabriella Csurka
7. Michael R. Lyu
8. Steffen Rendle

In Transportation

1. Qiang Yang
2. Han Yu
3. Yu Zheng

Citations in the corpus

• Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “[64] employ a generative convolutional autoencoder to guarantee class balance among distributed datasets used for in-home health monitoring.”
• Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, methodology): “Wu et al. (2022) proposed the FedHome framework, a novel cloud-edge-based federated learning framework for in-home health monitoring to training local models.”
• Towards Personalized Federated Learning (2021, methodology): “To model feature distribution skew, a dataset that is partitioned by users is often used with each user associated with a different client [24,59].”