most citedCompare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data

3 citations · 3 across the 2 of their papers we have counts for

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2 papers

cs.LG2021★ 3 cited

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data

Ha Min Son, Moon Hyun Kim, Tai-Myoung Chung

Federated Learning is a widely adopted method to train neural networks over distributed data. One main limitation is the performance degradation that occurs when data is heterogene…

cs.LG2021

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Joo Hun Yoo, Ha Min Son, Hyejun Jeong +5

While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on…