# Resilient Distributed Estimation Through Adversary Detection

arXiv: https://arxiv.org/abs/1701.00878 · HTML: https://mlsanity.com/paper/1701.00878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks](https://mlsanity.com/paper/2206.12749/) (2022)
- [VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence](https://mlsanity.com/paper/2405.07316/) (2024)

## Abstract (arXiv)

This paper studies resilient multi-agent distributed estimation of an unknown vector parameter when a subset of the agents is adversarial. We present and analyze a Flag Raising Distributed Estimator (FRDE) that allows the agents under attack to perform accurate parameter estimation and detect the adversarial agents. The FRDE algorithm is a consensus+innovations estimator in which agents combine estimates of neighboring agents (consensus) with local sensing information (innovations). We establish that, under FRDE, either the uncompromised agents' estimates are almost surely consistent or the uncompromised agents detect compromised agents if and only if the network of uncompromised agents is connected and globally observable. Numerical examples illustrate the performance of FRDE.