116 citations · 175 across the 24 of their papers we have counts for
3 papers · 1 filter
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…
Scalable Weight Reparametrization for Efficient Transfer Learning
Byeonggeun Kim, Jun-Tae Lee, Seunghan yang +1
This paper proposes a novel, efficient transfer learning method, called Scalable Weight Reparametrization (SWR) that is efficient and effective for multiple downstream tasks. Effic…
Robust Federated Learning with Noisy Labels
Seunghan Yang, Hyoungseob Park, Junyoung Byun +1
Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local dat…