Distributed robust estimation and inference with contaminated data

of observations from an arbitrary distribution. In a linear model setting, we develop a communication-efficient M-estimator that achieves the optimal convergence rate of centralized data. For robust inference, we introduce a distributed multiplier bootstrap method that requires no additional communication post-estimation while maintaining efficiency. To handle high contamination proportions, we present a debiasing procedure to mitigate bias. Extensive simulations demonstrate the robustness and scalability of our methods across diverse contamination scenarios.