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 Data heterogeneity under Non-Independent and Identically Distributed (Non-IID) settings remains one of the major challenges in Personalized Federated Learning (PFL) because it causes inconsistent local optimization, insufficient global knowledge transfer, and degraded personalization performance. To address these challenges, this paper proposes FedFACO, a personalized federated learning method based on the Fisher Information Matrix (FIM) for Adaptive Aggregation (AA) and Client Collaborative Optimization (CO). The proposed method improves model adaptation to heterogeneous client data distributions while preserving effective global knowledge sharing. By integrating AA with CO, FedFACO achieves a better balance between global generalization and local personalization in complex Non-IID environments. Methods FedFACO consists of two complementary components. First, the AA mechanism dynamically adjusts the fusion weights between the global and local models to generate personalized model initialization that better matches local data distributions. Second, the CO mechanism combines Parameter Alignment (PA) and Client Synchronization (CS). The PA strategy performs feature alignment and global regularization to strengthen global knowledge transfer while preserving local feature representations. The CS strategy uses FIM to quantify the information content of each client update and performs weighted aggregation to suppress low-quality updates. The proposed method is evaluated on the MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets under Non-IID settings and is compared with representative baseline methods. Convergence behavior, robustness to stragglers, and resilience to low-quality updates are also evaluated. Results and Discussions Experimental results demonstrate that FedFACO consistently outperforms representative baseline methods on all four benchmark datasets, achieving an average accuracy improvement of approximately 3.1% over existing methods (Fig. 1 and Table 2). On the more challenging Tiny-ImageNet dataset, FedFACO reduces the total training time required for convergence by approximately 4.8% compared with the best-performing baseline (Table 3). Ablation experiments demonstrate that both AA and CO substantially improve performance, and their combined use achieves the highest accuracy (Table 6). Furthermore, the FIM-based CS strategy accurately quantifies the information content of client updates in straggler and asynchronous training scenarios, thereby improving aggregation reliability (Figs. 2 and 3). The proposed method also exhibits superior robustness in the presence of malicious clients (Fig. 4). Conclusions This paper proposes FedFACO, a personalized federated learning method for Non-IID environments based on FIM for AA and CO. The proposed method effectively balances global knowledge sharing and local personalization while improving training stability and robustness under heterogeneous client participation. Experimental results demonstrate its advantages in prediction accuracy, convergence efficiency, and robustness. Future work will investigate lightweight FIM approximation methods and adaptive triggering strategies to reduce computational overhead and integrate the proposed framework with privacy-preserving and security 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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