Minimax rate of robust estimation when different samples have different but known rates of corruption.

We introduced and analyzed two novel gossip algorithms for rank and trimmed means estimation, proving convergence rates of $\mathcal{O}(1/t)$.

We theoretically characterize MoM's optimality for different classes of distributions under adversarial contamination