App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Federated learning framework with mixture-of-domain-experts and client-contribution-based aggregation

Li Ruping1
Wang Can1
Wu Qiuxin1
Wu Wenxuan1
Qin Yu2
1. School of Applied Science, Beijing Information Science & Technology University, Beijing 100192, China
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0172

Abstract

In practical federated learning scenarios, it is common that a single client contains data from multiple domains. Existing federated learning models mostly adopt a single feature extractor structure, whose fixed parameter capacity is insufficient to fully capture the feature distributions of multiple heterogeneous domains, leading to cross-domain representation conflicts. To address the above problems, this paper proposed FedMoDE (Federated Mixture-of-Domain-Experts) , a federated learning framework based on a mixture of domain experts and client-contribution-based aggregation. This framework designed the feature extraction component as a set of parallel expert models, with each expert specializing in learning features from a specific domain. To address the challenge of aggregating expert models, the framework designed a client-contribution-based aggregation strategy. This strategy estimated the sample size of each domain within a client based on the sample assignment probabilities output by the gating network, and then used this to perform weighted aggregation of experts responsible for the same domain, thereby achieving same-domain knowledge integration and cross-client alignment of expert models. Experimental results on the Digits and PACS datasets demonstrated that the proposed method significantly outperformed baseline methods, and the experimental results validated the effectiveness of each component. Through synergistic improvements in model architecture and aggregation strategy, this framework effectively enhances the performance of global models in multi-domain heterogeneous scenarios.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0172
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-22] Accepted Paper

Cite This Article

李如萍, 王灿, 吴秋新, 等. 基于混合域专家模型与客户端贡献度聚合的联邦学习框架 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0172. (Li Ruping, Wang Can, Wu Qiuxin, et al. Federated learning framework with mixture-of-domain-experts and client-contribution-based aggregation [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0172. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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