A min-rule distributed learning algorithm for hypothesis testing achieves network-independent exponential convergence rates and Byzantine resilience, outperforming belief-averaging methods.
Information heterogeneity and the speed of learning in social networks,
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A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience
A min-rule distributed learning algorithm for hypothesis testing achieves network-independent exponential convergence rates and Byzantine resilience, outperforming belief-averaging methods.