A Federated Learning Framework for Healthcare IoT devices

By Binhang Yuan, Song Ge, Wenhui Xing. 2020

85 citations recorded by the source corpus.

The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devices and a centralized server. Then most of the training computation is handled by the powerful server. The sparsification of activations and gradients significantly reduces the communication overhead. Empirical study have suggested that the proposed framework guarantees a low accuracy loss, while only requiring 0.2% of the synchronization traffic in vanilla federated learning.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

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. Jy-yong Sohn
2. Dimitris Papailiopoulos
3. Hongyi Wang
4. Shashank Rajput
5. Aruna Prasad Seneviratne
6. Jun Li
7. Ming Ding
8. Dinh C. Nguyen

In Biomedical Engineering

1. Dimitris Papailiopoulos
2. Dinh C. Nguyen
3. Hongyi Wang

In Computer Networks and Communications

1. Jun Li
2. Dinh C. Nguyen
3. Pubudu N. Pathirana
4. Ming Ding
5. Dimitris Papailiopoulos
6. Aruna Prasad Seneviratne
7. Hongyi Wang
8. Shashank Rajput

Citations in the corpus

• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020, background): “Several current applications of FL include text prediction in mobile device messaging [1, 2, 3, 4, 5], speech recognition [6], face recognition for device access [7, 8], and maintaining decentralized predictive models across health organizations [9, 10, 11].”
• Federated Learning for Healthcare Informatics (2019, unknown): “Arrhythmia detection [110] NN 16, 32, 64 PhysioNet Dataset [21]”
• Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “[118] Healthcare cooperation HFL NN Healthcare IoT devices Data server An efficient FLbased model for healthcare.”
• A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, unknown): “Yuan et al. [214] developed an FL framework for smart healthcare by applying the FL mechanism and reduced computation load of IoT devices during training.”