Global Federated Learning Strategies for Building Efficient Personalized Models
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental ...
| Authors | Seongyoon Kim |
|---|---|
| Primary category | cs.LG |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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