FedFACO: Personalized Federated Learning Method Based on Fisher Information Matrix for Adaptive Aggregation and Client Collaborative Optimization

JIANG Wei-Jin; LIU Zhi-Hua; CUI Xin-Yu; XU Yu-Sheng; CHEN Shen-You; HU Jia-Long

doi:10.11999/JEIT260344

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JIANG Wei-Jin, LIU Zhi-Hua, CUI Xin-Yu, XU Yu-Sheng, CHEN Shen-You, HU Jia-Long. FedFACO: Personalized Federated Learning Method Based on Fisher Information Matrix for Adaptive Aggregation and Client Collaborative Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260344

Citation:

JIANG Wei-Jin, LIU Zhi-Hua, CUI Xin-Yu, XU Yu-Sheng, CHEN Shen-You, HU Jia-Long. FedFACO: Personalized Federated Learning Method Based on Fisher Information Matrix for Adaptive Aggregation and Client Collaborative Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260344

Citation:

JIANG Wei-Jin, LIU Zhi-Hua, CUI Xin-Yu, XU Yu-Sheng, CHEN Shen-You, HU Jia-Long. FedFACO: Personalized Federated Learning Method Based on Fisher Information Matrix for Adaptive Aggregation and Client Collaborative Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260344

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FedFACO: Personalized Federated Learning Method Based on Fisher Information Matrix for Adaptive Aggregation and Client Collaborative Optimization

doi: 10.11999/JEIT260344 cstr: 32379.14.JEIT260344

JIANG Wei-Jin^{1, 3},
LIU Zhi-Hua^{1, 3
,
,},
CUI Xin-Yu^{1, 3},
XU Yu-Sheng²,
CHEN Shen-You^{1, 3},
HU Jia-Long^{1, 3}

1.
School of Computer Science, Hunan University of Technology and Business, Changsha, Hunan 410205, China
2.
Northern Navigation Control Technology Co., Ltd, Beijing 100176, China
3.
Xiangjiang Laboratory, Changsha 410205, China

Funds: The National Natural Science Foundation of China (61772196), Natural Science Foundation of Hunan Province (2020JJ4249), Key Scientific Research Project of Hunan Provincial Department of Education (24A0446, 24A0753), Hunan Provincial Graduate Student Research Innovation Project (CX20251694)

Received Date: 2026-03-24
Accepted Date: 2026-07-09
Rev Recd Date: 2026-07-09

Available Online: 2026-07-23

Abstract

Abstract

Objective Non-IID data heterogeneity remains one of the major challenges in personalized federated learning, as it often leads to inconsistent local optimization directions, insufficient global knowledge transfer, and degraded model personalization performance. To address these issues, this paper proposes FedFACO, a personalized federated learning method based on Fisher information matrix-guided adaptive aggregation and client collaborative optimization. The proposed method aims to improve the adaptability of federated models to heterogeneous client distributions while maintaining effective knowledge sharing across clients. By introducing an adaptive aggregation mechanism and a collaborative optimization strategy, FedFACO provides a more principled way to balance global generalization and local personalization, which is particularly important in complex Non-IID federated environments. Methods FedFACO consists of two key components. First, an adaptive aggregation (AA) mechanism is employed to dynamically adjust fusion weights between global and local models based on client-specific states, generating personalized initializations aligned with local data. Second, a collaborative optimization (CO) mechanism is introduced, combining feature alignment with FIM-based client weighting. This enhances useful global knowledge transfer and suppresses low-quality updates. The FIM is utilized to quantify the information contribution of each update, ensuring reliable aggregation. The method is evaluated on MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet under Non-IID settings, and compared with representative baselines. Convergence behavior, dropout robustness, and sensitivity to low-quality updates are also examined. Results and Discussions Experimental results demonstrate that FedFACO consistently outperforms competitive baseline methods across all four benchmark datasets, achieving an average accuracy improvement of approximately 3.1% over mainstream approaches (Fig. 1, Table 2). On the more challenging Tiny-ImageNet dataset, FedFACO reduces the total training time required to reach convergence by approximately 4.8% compared with the best baseline (Table 3). Ablation studies confirm that performance is substantially improved by both AA and CO mechanisms, with their joint application yielding optimal accuracy (Table 6). Furthermore, FIM-guided weighting is shown to accurately quantify contribution quality in client dropout and asynchronous scenarios, significantly enhancing aggregation reliability (Fig. 2–Fig. 3). Superior robustness is also demonstrated in malicious client scenarios (Fig. 4). Conclusions This paper presents FedFACO, a personalized federated learning method for Non-IID environments via Fisher information matrix-guided adaptive aggregation and client collaborative optimization. The method effectively balances global knowledge sharing and local personalization while enhancing training stability and robustness under heterogeneous client participation. Experimental results validate its effectiveness and superiority in accuracy, convergence efficiency, and robustness. Future work will focus on lightweight Fisher information approximations and adaptive triggering strategies to reduce computational overhead, as well as integration with privacy-preserving and security-defense mechanisms for deployment in resource-constrained and high-security environments.
- Federated learning,
- Data heterogeneity,
- Fisher information matrix,
- Adaptive aggregation,
- Collaborative optimization,
- Personalization

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References(25)

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