Robust Online and Distributed Mean Estimation Under Adversarial Data Corruption

• URL: http://arxiv.org/abs/2209.09624v1
• Date: Sat, 17 Sep 2022 16:36:21 GMT
• Title: Robust Online and Distributed Mean Estimation Under Adversarial Data Corruption
• Authors: Tong Yao and Shreyas Sundaram
• Abstract summary: We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks. We provide the error-bound and the convergence properties of the estimates to incorporate the true mean under our algorithms.
• Score: 1.9199742103141069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks. At each time step, each agent in a network receives a potentially corrupted data point, where the data points were originally independent and identically distributed samples of a random variable. We propose online and distributed algorithms for all agents to asymptotically estimate the mean. We provide the error-bound and the convergence properties of the estimates to the true mean under our algorithms. Based on the network topology, we further evaluate each agent's trade-off in convergence rate between incorporating data from neighbors and learning with only local observations.

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