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cs.LG2023★ 29 cited
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Minkyung Kim, Jongmin Yu, Junsik Kim +2
Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it…
cs.LG2021★ 16 cited
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning
Nam Hyeon-Woo, Moon Ye-Bin, Tae-Hyun Oh
In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our metho…