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Furthermore, a collaborative attention strategy is developed to enable privacy-preserving semantic knowledge exchange across clients. To alleviate the class imbalance and overfitting problems common in few-shot settings, we design an improved Focal Loss function guided by class-wise entropy. Additionally, a progressive knowledge distillation framework is incorporated to maintain model consistency across training rounds. Extensive experiments conducted on multiple few-shot image benchmarks under federated settings demonstrate that our method significantly outperforms existing baselines in terms of accuracy, communication efficiency, and generalization. <\/jats:p>","DOI":"10.1142\/s0218001425510152","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:11:24Z","timestamp":1749773484000},"source":"Crossref","is-referenced-by-count":0,"title":["A Transformer-Based Method for Few-Shot Image Recognition in Federated Learning Environments"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6720-7271","authenticated-orcid":false,"given":"Guodong","family":"Wang","sequence":"first","affiliation":[{"name":"Shandong Xiehe University, Jinan 250107, P. R. 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