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Related Experiment Videos

DynamicFU: Contribution-Aware Dynamic Federated Unlearning for Industrial IoT.

Ziang Wu¹, Buzhen He², Zhiwei Si¹

¹Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

DynamicFU enables efficient federated unlearning in Industrial Internet of Things (IIoT) by considering client data contributions. This approach significantly speeds up model updates while maintaining effectiveness.

Keywords:

Industrial Internet of Things federated learning federated unlearning privacy-preserving

Related Experiment Videos

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Federated learning (FL) is crucial for Industrial Internet of Things (IIoT) collaborative training without raw data sharing.
Data removal requests (e.g., GDPR) necessitate federated unlearning in IIoT.
Imbalanced client data in IIoT leads to varying client contributions to global models.

Purpose of the Study:

To develop a federated unlearning framework that accounts for individual client contributions in IIoT.
To address the limitations of uniform unlearning strategies in imbalanced IIoT data distributions.

Main Methods:

Propose DynamicFU, a contribution-aware dynamic federated unlearning framework for IIoT.
Evaluate target clients using parameter-level, data-level, and performance-level metrics.
Dynamically adjust unlearning strength by modifying the number of unlearning rounds.

Main Results:

DynamicFU significantly enhances federated unlearning efficiency in IIoT.
Achieved up to 22.89× speedup compared to Full Retrain.
Maintained comparable effectiveness to existing methods.

Conclusions:

DynamicFU offers an effective and efficient solution for federated unlearning in IIoT.
The contribution-aware dynamic approach is vital for handling data imbalance and ensuring regulatory compliance.