Ke Li1,*, Xiaofeng Wang1,2,*, Hu Wang1
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1391-1407, 2024, DOI:10.32604/cmc.2024.054484
- 15 October 2024
Abstract In the realm of data privacy protection, federated learning aims to collaboratively train a global model. However, heterogeneous data between clients presents challenges, often resulting in slow convergence and inadequate accuracy of the global model. Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution. Nonetheless, previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers, thereby limiting model performance. To tackle these issues, this study proposes a hierarchical optimization method for federated learning with feature alignment… More >
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