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Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks

By Guanqiang Zhou and others
In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to byzantine attacks which could invalidate... Show more
October 29, 2022
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Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks
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