2 citations · 2 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
Myoungjun Kim, Sangwoo Park, Yoseob Han +1
Differentially private federated fine-tuning of large models with LoRA suffers from aggregation error caused by LoRA's multiplicative structure, which is further amplified by DP no…
cs.LG2025
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.LG2023
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
Jiyun Shin, Jinhyun Ahn, Honggu Kang +1
Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources. Federated learning (FL) address…