Federated Transfer Learning for Cross-Domain Image Classification
Abstract
Cross-domain image classification often suffers from data scarcity and domain divergence. This paper explores the use of federated transfer learning to address these challenges by enabling collaborative model training across distributed datasets without data sharing. The proposed method combines feature extraction from pre-trained models with domain adaptation techniques to enhance classification performance. Experiments on healthcare and satellite imagery datasets show improved accuracy and reduced training time, demonstrating the potential of federated transfer learning for privacy-preserving, cross-domain applications.
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