Personalized federated prototype learning in mixed heterogeneous data scenarios
This addresses performance issues in federated learning for privacy-sensitive applications, though it appears incremental as it builds on existing prototype-based methods.
The paper tackles the problem of data heterogeneity in federated learning by proposing PFPL, a personalized federated prototype learning approach for mixed heterogeneous scenarios that constructs unbiased prototypes and uses consistent regularization, achieving improved convergence and reduced communication costs on Digits and Office Caltech datasets.
Federated learning has received significant attention for its ability to simultaneously protect customer privacy and leverage distributed data from multiple devices for model training. However, conventional approaches often focus on isolated heterogeneous scenarios, resulting in skewed feature distributions or label distributions. Meanwhile, data heterogeneity is actually a key factor in improving model performance. To address this issue, we propose a new approach called PFPL in mixed heterogeneous scenarios. The method provides richer domain knowledge and unbiased convergence targets by constructing personalized, unbiased prototypes for each client. Moreover, in the local update phase, we introduce consistent regularization to align local instances with their personalized prototypes, which significantly improves the convergence of the loss function. Experimental results on Digits and Office Caltech datasets validate the effectiveness of our approach and successfully reduce the communication cost.