A Federated Learning Framework for Healthcare IoT devices

Abstract

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.

References in corpus (6)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Sparsified SGD with Memory
• Split learning for health: Distributed deep learning without sharing raw patient data
• Federated Learning for Healthcare Informatics
• Detailed comparison of communication efficiency of split learning and federated learning

Cited by in corpus (4)

• Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
• Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus
• FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences