15 citations · 30 across the 6 of their papers we have counts for
7 papers · 1 filter
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
Seongyoon Kim, Gihun Lee, Jaehoon Oh +1
Federated Learning (FL) is a collaborative method for training models while preserving data privacy in decentralized settings. However, FL encounters challenges related to data het…
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Gihun Lee, Minchan Jeong, Sangmook Kim +2
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers…
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Jin-woo Lee, Jaehoon Oh, Yooju Shin +2
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distribute…
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Jin-woo Lee, Jaehoon Oh, Sungsu Lim +2
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much cons…
BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim +1
Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates…