3 citations · 4 across the 5 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.LG2026
Global Federated Learning Strategies for Building Efficient Personalized Models
Seongyoon Kim
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 ha…
cs.LG2026
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Seongyoon Kim, Boryeong Cho, Jihwan Oh +2
Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be centralized. Federated learning…