Federated Contrastive Learning With Feature-Based Distillation for Human Activity Recognition

This article proposes a federated contrastive learning with feature-based distillation (FCLFD) framework tailored for human activity recognition (HAR). The FCLFD system integrates a central server with multiple mobile users to address a diverse range of HAR challenges. The framework encompasses two pivotal elements: a contrastive student--teacher (CST) architecture with feature-based distillation and an average weight scheme (AWS). The CST framework facilitates the transfer of comprehensive knowledge from a teacher model to a student model through feature-based distillation and contrastive learning, with both models sharing an identical architecture. Each participating user periodically uploads the weights of…

• NN
  National Natural Science Foundation of China

  Award: 72401128
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK20240537
• SS
  Social Science Foundation of Jiangsu Province

  Award: 23GLLC015