Multi-scale information fusion and layered attention aggregation for subgraph federated learning algorithm

WANG Ruoyu, DING Shifei, GUO Lili

doi:10.3969/j.issn.1007-130X.2026.05.016

Computer Engineering & Science >

2026 , Vol. 48 >Issue 5: 925 - 935

DOI: https://doi.org/10.3969/j.issn.1007-130X.2026.05.016

Artificial Intelligence and Data Mining

Multi-scale information fusion and layered attention aggregation for subgraph federated learning algorithm

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(1.School of Computer Science and Technology/School of Artificial Intelligence,
China University of Mining and Technology,Xuzhou 221116；
2.Mine Digitization Engineering Research Center of the Ministry of Education,
China University of Mining and Technology,Xuzhou 221116,China)

Received date: 2025-07-29

Revised date: 2025-09-11

Online published: 2026-05-21

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Abstract

Subgraph federated learning trains on subgraphs of a global graph on local clients using graph convolutional networks and updates these parameters on the server, thereby safeguarding user privacy. Existing methods lack attention to important nodes in specific tasks or graph structures, which may reduce the efficiency of node embedding. This paper proposes a novel graph federated learning algorithm, called FedMFG, which introduces multi-scale information fusion convolution to integrate node features with neighbor information, thereby enhancing node feature representation capabilities. The algorithm passes parameters during the pre-training phase to reduce communication costs and applies an attention mechanism at the server to dynamically adjust weights for better aggregation of global parameters. Experimental results on standard benchmark datasets demonstrate that FedMFG achieves higher accuracy, higher stability, and lower communication costs than previous algorithms.

Key words： subgraph federated learning; multi-scale information fusion convolution; graph convolutional network(GCN) model; attention mechanism

Cite this article

WANG Ruoyu, DING Shifei, GUO Lili . Multi-scale information fusion and layered attention aggregation for subgraph federated learning algorithm[J]. Computer Engineering & Science, 2026 , 48(5) : 925 -935 . DOI: 10.3969/j.issn.1007-130X.2026.05.016

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