Distributed ML with partitioned data and independently tuned learners improves robustness against transfer-based attacks over an ensemble baseline, but the abstract's emphasis on full heterogeneity is contradicted by the paper's regression results.
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On the Robustness of Distributed Machine Learning against Transfer Attacks
Distributed ML with partitioned data and independently tuned learners improves robustness against transfer-based attacks over an ensemble baseline, but the abstract's emphasis on full heterogeneity is contradicted by the paper's regression results.