# Resilient Distributed Estimation: Sensor Attacks

arXiv: https://arxiv.org/abs/1709.06156 · HTML: https://mlsanity.com/paper/1709.06156/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [Communication-efficient Distributed Cooperative Learning with Compressed Beliefs](https://mlsanity.com/paper/2102.07767/) (2021)
- [Decentralized Online Regularized Learning Over Random Time-Varying Graphs](https://mlsanity.com/paper/2206.03861/) (2022)
- [Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks](https://mlsanity.com/paper/2206.12749/) (2022)

## Abstract (arXiv)

This paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at every time step, a fraction of the agents' sensor measurements may fall under attack and take arbitrary values. We present the Saturated Innovation Update (SIU) algorithm for distributed estimation resilient to sensor attacks. Under the iterative SIU algorithm, if less than one half of the agent sensors fall under attack, then, all of the agents' estimates converge at a polynomial rate (with respect to the number of iterations) to the true parameter. The resilience of SIU to sensor attacks does not depend on the topology of the inter-agent communication network, as long as it remains connected. We demonstrate the performance of SIU with numerical examples.