2 papers
cs.LG2026
FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels
Seunghun Yu, Jin-Hyun Ahn, Joonhyuk Kang
Federated Learning (FL) is a powerful framework for privacy-preserving distributed learning. It enables multiple clients to collaboratively train a global model without sharing raw…
cs.LG2025
GeFL: Model-Agnostic Federated Learning with Generative Models
Honggu Kang, Seohyeon Cha, Joonhyuk Kang
Federated learning (FL) is a distributed training paradigm that enables collaborative learning across clients without sharing local data, thereby preserving privacy. However, the i…