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A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation
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作者 Hui WANG Jie SUN +2 位作者 Tianyu WO Xudong LIU Suzhen PEI 《Frontiers of Computer Science》 2025年第6期141-143,共3页
1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of... 1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of a shared representation model without compromising individual participants’data privacy[1,2].However,the continuous learning process may cause catastrophic forgetting in the model,reducing generated representations’performance. 展开更多
关键词 federated learning fl frameworkwhich training shared representation model gradient truncation learning paradigm hybrid model architecture unsupervised representation techniques catastrophic forgetting unsupervised federated continual learning
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