arXiv Machine Learning August 18, 2026 By Seongyoon Kim

Global Federated Learning Strategies for Building Efficient Personalized Models

Read the original on arXiv Machine Learning →

arXiv:2608. 15107v1 Announce Type: new Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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