计算机工程2026，Vol.52Issue(5)：129-138,10.DOI:10.19678/j.issn.1000-3428.0070288
结合旋转自监督和CLIP指导的长尾数据联邦学习
Federated Learning on Long-Tail Data Combining Rotational Self-Supervision and CLIP Guidance
摘要
Abstract
Real-world data often follow a long-tail distribution.Federated learning methods that assume a balanced global data distribution struggle to classify tail-class data within long-tail data accurately.Researchers typically focus on retraining a balanced classifier for the global model,to mitigate the impact of long-tail data.However,this approach does not consider the feature extractor of the balanced model or how the model's feature extractor can be enabled to learn high-quality image features,leading to the poor performance of the global model.To enable the model to learn high-quality image features without bias during the feature learning stage,this study proposes a federated learning method combining rotational self-supervision and Contrastive Language-Image Pre-training(CLIP)guidance.This method uses rotational self-supervision to guide the training of local client models,thereby reducing the impact of long-tail data on the client models and enabling the model to learn high-quality image features.Simultaneously,CLIP is utilized to guide both the normal training of the model and the rotated images,transferring rich knowledge from CLIP to the client model and further enhancing the performance of the feature extractor.In experiments on the CIFAR-10 and CIFAR-100 datasets under different long-tail distributions,the proposed approach improves the global model's classification accuracy by 2.35 to 4.72 percentage points,respectively,compared with other federated learning methods.关键词
联邦学习/长尾分布/异构数据/自监督学习/对比语言-图像预训练Key words
federated learning/long-tailed distribution/heterogeneous data/self-supervised learning/Contrastive Language-Image Pre-training(CLIP)分类
信息技术与安全科学引用本文复制引用
刘海军,付晓东..结合旋转自监督和CLIP指导的长尾数据联邦学习[J].计算机工程,2026,52(5):129-138,10.基金项目
国家自然科学基金(62362043) （62362043）
云南省"兴滇英才支持计划"项目(KKXY202203008) （KKXY202203008）
云南省科技计划项目(202205AF150003,202204BQ040010,202102AD080002). （202205AF150003,202204BQ040010,202102AD080002）
