4 papers
Personalized Federated Learning for Gradient Alignment
Dongwon Kim, Gyuejeong Lee
Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized information. Local training i…
TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments
Gyuejeong Lee, Daeyoung Choi
Communication efficiency in federated learning (FL) remains a critical challenge for resource-constrained environments. While prototype-based FL reduces communication overhead by s…
Heterogeneous Federated Learning with Prototype Alignment and Upscaling
Gyuejeong Lee, Jihwan Shin, Daeyoung Choi
Heterogeneity in data distributions and model architectures remains a significant challenge in federated learning (FL). Various heterogeneous FL (HtFL) approaches have recently bee…
Class-Wise Federated Averaging for Efficient Personalization
Gyuejeong Lee, Daeyoung Choi
Federated learning (FL) enables collaborative model training across distributed clients without centralizing data. However, existing approaches such as Federated Averaging (FedAvg)…