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Resource-efficient federated machine unlearning via evolutionary synaptic pruning for cloud-based distributed
Himani Bansal1, Bhuvan Unhelkar2, Dilip Kumar Jang Bahadur Saini3
1Jaypee Institute of Information Technology, Noida, India. singal.himani@gmail.com.
Scientific Reports
|May 18, 2026
Summary
Federated Unlearning (FU) efficiently removes user data influence in AI models without full retraining. PRUNE-FL uses synaptic relevance and evolutionary pruning for privacy preservation and improved accuracy, even against backdoor attacks.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Data Privacy
Background:
- Growing emphasis on user data control and privacy regulations (e.g., GDPR) necessitates effective machine unlearning (MU).
- Federated Learning (FL) presents unique challenges for unlearning due to distributed data, leading to Federated Unlearning (FU).
- Retraining models for data deletion requests in large-scale FL is computationally expensive and disruptive.
Purpose of the Study:
- To propose PRUNE-FL, a novel framework for privacy-preserving Federated Unlearning.
- To develop a method that efficiently deletes targeted data influence without full model retraining.
- To balance model performance with effective data forgetting in FL settings.
Main Methods:
- PRUNE-FL employs relevance-guided pruning and evolutionary optimization.
- A synaptic relevance scoring system identifies parameters linked to target data.
- A multi-objective problem formulation balances performance and forgetting, optimized by a genetic algorithm.
Main Results:
- PRUNE-FL demonstrates higher accuracy on the CIFAR-10 dataset in both IID and non-IID settings.
- The framework effectively removes the influence of targeted data.
- PRUNE-FL shows robustness against backdoor triggers.
Conclusions:
- PRUNE-FL offers an efficient solution for Federated Unlearning, enhancing privacy.
- The method reduces computational costs and resource usage in federated environments.
- PRUNE-FL selectively unlearns data while maintaining model performance and security.
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