2 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Cooperative Learning via Federated Distillation over Fading Channels
Jin-Hyun Ahn, Osvaldo Simeone, Joonhyuk Kang
Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Fede…
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
Jin-Hyun Ahn, Osvaldo Simeone, Joonhyuk Kang
Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenge…