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CAFL: Conditional Attention Federated Learning for Image Emotion Analysis
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Chang Liu1, 2, 3, 4, Zengmao Wang1, 2, 3, 4, Yongchao Xu1, 2, 3, 4, Bo Du1, 2, 3, 4
Data Science and Engineering | 2026, 11(1) : 66 - 83
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Data Science and Engineering | 2026, 11(1): 66-83
RESEARCH PAPERS
CAFL: Conditional Attention Federated Learning for Image Emotion Analysis
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Chang Liu1, 2, 3, 4, Zengmao Wang1, 2, 3, 4, Yongchao Xu1, 2, 3, 4, Bo Du1, 2, 3, 4
Affiliations
  • 1School of Computer Science, Wuhan University, Wuhan 430072, China
  • 2Institute of Artificial Intelligence, Wuhan University, Wuhan 430072, China
  • 3National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, China
  • 4Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China
  • Yongchao Xu 

Published: 2026-03-01 doi: 10.1007/s41019-025-00315-9
Outline
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The rapid proliferation of images on online platforms has made emotion analysis a task of paramount significance. However, these images are often privacy-sensitive, making Federated Learning (FL) a compelling paradigm over traditional centralized methods. A critical yet largely unaddressed challenge in applying FL to this domain is the severe concept drift stemming from the subjective and culturally diverse nature of emotional expression, which causes conventional FL algorithms to fail. In this paper, we propose CAFL (Conditional Attention Federated Learning) to fill this gap. CAFL empowers clients to learn collaboratively yet personally. It intelligently routes information through an adaptive gate that separates features into a personalized stream and a global stream. These streams are then processed by dedicated local and global prediction heads. Crucially, collaboration is guided by a conditional attention mechanism, where the server computes a personalized reference model for each client based on an attention-weighted aggregation of peer models, promoting knowledge sharing among kindred clients. Extensive experiments on various lightweight foundation models show that CAFL consistently outperforms existing FL methods, demonstrating its robustness and superior performance as a solution for distributed, privacy-sensitive image emotion analysis.

Federated Learning  /  Personalized Federated Learning  /  Foundation Models  /  Image Emotion
Chang Liu, Zengmao Wang, Yongchao Xu, Bo Du. CAFL: Conditional Attention Federated Learning for Image Emotion Analysis[J]. Data Science and Engineering, 2026 , 11 (1) : 66 -83 . DOI: 10.1007/s41019-025-00315-9
  • National Natural Science Foundation of China(62225113; 62222112; 62176186)
Year 2026 volume 11 Issue 1
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Article Info
doi: 10.1007/s41019-025-00315-9
  • Receive Date:2024-09-09
  • Online Date:2026-08-06
  • Published:2026-03-01
Article Data
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History
  • Received:2024-09-09
  • Revised:2025-07-15
  • Accepted:2025-08-18
Funding
National Natural Science Foundation of China(62225113; 62222112; 62176186)
Affiliations
    1School of Computer Science, Wuhan University, Wuhan 430072, China
    2Institute of Artificial Intelligence, Wuhan University, Wuhan 430072, China
    3National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, China
    4Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China

Corresponding:

Zengmao Wang 
Bo Du 
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