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Scoring Aided Federated Learning on Long-Tailed Data for Wireless IoMT Based Healthcare System.

Lianhong Zhang, Yuxin Wu, Lunyuan Chen

IEEE Journal of Biomedical and Health Informatics

|August 2, 2023

Summary

This study introduces a new federated learning (FL) framework for medical Internet of Things (IoMT) systems. It improves model accuracy on imbalanced datasets and unreliable wireless channels by intelligently selecting clients for training.

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Area of Science:

Computer Science
Artificial Intelligence
Healthcare Technology

Background:

Federated learning (FL) in wireless Internet of Medical Things (IoMT) systems faces challenges with long-tailed data, leading to biased global models.
Severe wireless channel fading can exclude mobile clients from model aggregation, further degrading FL performance.

Purpose of the Study:

To propose a novel scoring-aided FL framework to address data imbalance and unreliable wireless conditions in IoMT healthcare systems.
To enhance the performance of FL by selecting mobile clients with more tail data and better transmission quality.

Main Methods:

A logits-based scoring client selection method is proposed to mitigate the impact of long-tailed data by exploring local data distribution.
Channel State Information (CSI) and data rate are incorporated into a scoring mechanism for client selection, addressing wireless fading issues.
A novel client selection method combining logits and model upload rate is developed to optimize FL performance.

Main Results:

The proposed framework significantly improves FL performance compared to conventional FedAvg.
Accuracy gains ranging from 4.44% to 28.36% were achieved on the CIFAR-10-LT dataset with an imbalance factor of 50.
The scoring-based sampling strategy effectively selects clients with valuable tail data and stable transmission conditions.

Conclusions:

The novel scoring-aided FL framework effectively overcomes challenges posed by long-tailed data and wireless channel fading in IoMT systems.
The proposed client selection methods enhance model accuracy and robustness in healthcare applications.
This work contributes to more reliable and efficient federated learning for medical applications.