# Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks

arXiv: https://arxiv.org/abs/2207.08870 · HTML: https://mlsanity.com/paper/2207.08870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2022.

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

- [Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching](https://mlsanity.com/paper/2405.03782/) (2024)
- [PFedDST: Personalized Federated Learning with Decentralized Selection Training](https://mlsanity.com/paper/2502.07750/) (2025)

## Abstract (arXiv)

In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision. In this work, we investigate the effect of data falsification attacks on the performance of EEOT-based systems. We derive the probability of error for an EEOT-based system under attack and find an upper bound (UB) on the expected number of transmissions required to make the final decision. Moreover, we tighten this UB by solving an optimization problem via integer programming (IP). We also obtain the FC's optimal threshold which guarantees the optimal detection performance of the EEOT-based system. Numerical and simulation results indicate that it is possible to reduce transmissions while still ensuring the quality of the decision with an appropriately designed threshold.