# Low-Communication Resilient Distributed Estimation Algorithm Based on Memory Mechanism

arXiv: https://arxiv.org/abs/2508.02705 · HTML: https://mlsanity.com/paper/2508.02705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Multitask Diffusion Adaptation over Networks](https://mlsanity.com/paper/1311.4894/) (2013)
- [Generalized Correntropy for Robust Adaptive Filtering](https://mlsanity.com/paper/1504.02931/) (2015)
- [Target Sensing with Intelligent Reflecting Surface: Architecture and Performance](https://mlsanity.com/paper/2201.09091/) (2022)
- [Analysis of a Reduced-Communication Diffusion LMS Algorithm](https://mlsanity.com/paper/1408.5845/) (2014)
- [Maximum Total Correntropy Diffusion Adaptation over Networks with Noisy Links](https://mlsanity.com/paper/1802.05144/) (2018)
- [6D Movable Antenna Based on User Distribution: Modeling and Optimization](https://mlsanity.com/paper/2403.08123/) (2024)
- [6D Movable Antenna Enhanced Wireless Network Via Discrete Position and Rotation Optimization](https://mlsanity.com/paper/2403.17122/) (2024)

## Abstract (arXiv)

In multi-task adversarial networks, the accurate estimation of unknown parameters in a distributed algorithm is hindered by attacked nodes or links. To tackle this challenge, this brief proposes a low-communication resilient distributed estimation algorithm. First, a node selection strategy based on reputation is introduced that allows nodes to communicate with more reliable subset of neighbors. Subsequently, to discern trustworthy intermediate estimates, the Weighted Support Vector Data Description (W-SVDD) model is employed to train the memory data. This trained model contributes to reinforce the resilience of the distributed estimation process against the impact of attacked nodes or links. Additionally, an event-triggered mechanism is introduced to minimize ineffective updates to the W-SVDD model, and a suitable threshold is derived based on assumptions. The convergence of the algorithm is analyzed. Finally, simulation results demonstrate that the proposed algorithm achieves superior performance with less communication cost compared to other algorithms.