Research on Federated Unlearning Approach Based on Adaptive Model Pruning

MA Zhenguo; HE Zixuan; SUN Yanjing; WANG Bowen; LIU Jianchun; XU Hongli

doi:10.11999/JEIT250503

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MA Zhenguo, HE Zixuan, SUN Yanjing, WANG Bowen, LIU Jianchun, XU Hongli. Research on Federated Unlearning Approach Based on Adaptive Model Pruning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250503

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MA Zhenguo, HE Zixuan, SUN Yanjing, WANG Bowen, LIU Jianchun, XU Hongli. Research on Federated Unlearning Approach Based on Adaptive Model Pruning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250503

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Research on Federated Unlearning Approach Based on Adaptive Model Pruning

doi: 10.11999/JEIT250503 cstr: 32379.14.JEIT250503

MA Zhenguo¹,
HE Zixuan²,
SUN Yanjing^{1
,
,},
WANG Bowen^{2, 3},
LIU Jianchun^{4, 5},
XU Hongli^{4, 5}

1.
School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
3.
IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou 221116, China
4.
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
5.
Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China

Funds: Fundamental Research Funds for the Central Universities (XJ2025012301), Shenzhen Science and Technology Program (KCXFZ20240903094204007)

Received Date: 2025-06-03
Rev Recd Date: 2025-08-05

Available Online: 2025-09-16

Abstract

Abstract

Objective The rapid proliferation of Internet of Things (IoT) devices and the enforcement of data privacy regulations, including the General Data Protection Regulation (GDPR) and the Personal Information Protection Act, have positioned Federated Unlearning (FU) as a critical mechanism to safeguard the “right to be forgotten” in Edge Computing (EC). Existing class-level unlearning approaches often adopt uniform model pruning strategies. However, because edge nodes vary substantially in computational capacity, storage, and network bandwidth, these methods suffer from efficiency degradation, leading to imbalanced training delays and decreased resource utilization. This study proposes FU with Adaptive Model Pruning (FunAMP), a framework that minimizes training time while reliably eliminating the influence of target-class data. FunAMP dynamically assigns pruning ratios according to node resources and incorporates a parameter correlation metric to guide pruning decisions. In doing so, it addresses the challenge of resource heterogeneity while preserving compliance with privacy regulations. Methods The proposed framework establishes a quantitative relationship among model training time, node resources, and pruning ratios, on the basis of which an optimization problem is formulated to minimize overall training time. To address this problem, a greedy algorithm (Algorithm 2) is designed to adaptively assign appropriate pruning ratios to each node. The algorithm discretizes the pruning ratio space and applies a binary search strategy to balance computation and communication delays across nodes. Additionally, a Term Frequency–Inverse Document Frequency (TF–IDF)-based metric is introduced to evaluate the correlation between model parameters and the target-class data. For each parameter, the TF score reflects its activation contribution to the target class, whereas the IDF score measures its specificity across all classes. Parameters with high TF–IDF scores are iteratively pruned until the assigned pruning ratio is satisfied, thereby ensuring the effective removal of target-class data. Results and Discussions Simulation results confirm the effectiveness of FunAMP in balancing training efficiency and unlearning performance under resource heterogeneity. The effect of pruning granularity on model accuracy (Fig. 2): fine granularity (e.g., 0.01) preserves model integrity, whereas coarse settings degrade accuracy due to excessive parameter removal. Under fixed training time, FunAMP consistently achieves higher accuracy than FunUMP and Retrain (Fig. 3), as adaptive pruning ratios reduce inter-node waiting delays. For instance, FunAMP attains 76.48% accuracy on LeNet and 83.60% on AlexNet with FMNIST, outperforming baseline methods by 5.91% and 4.44%, respectively. The TF–IDF-driven pruning mechanism fully removes contributions of target-class data, achieving 0.00% accuracy on the target data while maintaining competitive performance on the remaining data (Table 2). Robustness under varying heterogeneity levels is further verified (Fig. 4). Compared with baselines, FunAMP markedly reduces the training time required to reach predefined accuracy and delivers up to 11.8× speedup across four models. These results demonstrate FunAMP’s capability to harmonize resource utilization, preserve model performance, and ensure unlearning efficacy in heterogeneous edge environments. Conclusions To mitigate training inefficiency caused by resource heterogeneity in FU, this study proposes FunAMP, a framework that integrates adaptive pruning with parameter relevance analysis. A system model is constructed to formalize the relationship among node resources, pruning ratios, and training time. A greedy algorithm dynamically assigns pruning ratios to edge nodes, thereby minimizing global training time while balancing computational and communication delays. Furthermore, a TF–IDF-driven metric quantifies the correlation between model parameters and target-class data, enabling the selective removal of critical parameters to erase target-class contributions. Theoretical analysis verifies the stability and reliability of the framework, while empirical results demonstrate that FunAMP achieves complete removal of target-class data and sustains competitive accuracy on the remaining classes. This work is limited to single-class unlearning, and extending the approach to scenarios requiring the simultaneous removal of multiple classes remains an important direction for future research.
- Federated Unlearning (FU),
- Resource heterogeneity,
- Adaptive Model Pruning,
- Correlation metric,
- Performance analysis

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References(18)

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