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cs.LG2023
Efficient Quality-Diversity Optimization through Diverse Quality Species
Ryan Wickman, Bibek Poudel, Michael Villarreal +2
A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorit…
cs.LG2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang +2
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improv…