# Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks

arXiv: https://arxiv.org/abs/2206.12749 · HTML: https://mlsanity.com/paper/2206.12749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 77 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development](https://mlsanity.com/paper/1702.05122/) (2017)
- [ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning](https://mlsanity.com/paper/1708.08155/) (2017)
- [Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning](https://mlsanity.com/paper/2001.02112/) (2020)
- [Distributed Optimization Under Adversarial Nodes](https://mlsanity.com/paper/1606.08939/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Multitask Diffusion Adaptation over Networks](https://mlsanity.com/paper/1311.4894/) (2013)
- [Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents](https://mlsanity.com/paper/1812.09985/) (2018)
- [Byzantine-Robust Decentralized Stochastic Optimization over Static and Time-Varying Networks](https://mlsanity.com/paper/2005.06276/) (2020)
- [The Internet of Things: Secure Distributed Inference](https://mlsanity.com/paper/1806.09199/) (2018)
- [Diffusion LMS over Multitask Networks](https://mlsanity.com/paper/1404.6813/) (2014)
- [Distributed Clustering and Learning Over Networks](https://mlsanity.com/paper/1409.6111/) (2014)
- [Multitask diffusion adaptation over asynchronous networks](https://mlsanity.com/paper/1412.1798/) (2014)
- [Diffusion LMS for Multitask Problems with Local Linear Equality Constraints](https://mlsanity.com/paper/1610.02943/) (2016)
- [Multitask diffusion adaptation over networks with common latent representations](https://mlsanity.com/paper/1702.03614/) (2017)
- [Resilient Distributed Estimation: Sensor Attacks](https://mlsanity.com/paper/1709.06156/) (2017)
- [Byzantine-Resilient Distributed Observers for LTI Systems](https://mlsanity.com/paper/1802.09651/) (2018)
- [Recursive Geman-McClure method for implementing second-order Volterra filter](https://mlsanity.com/paper/1808.00613/) (2018)
- [Robust Adaptive Filtering Based on Exponential Functional Link Network](https://mlsanity.com/paper/2102.02952/) (2021)
- [Resilient Distributed Estimation Through Adversary Detection](https://mlsanity.com/paper/1701.00878/) (2017)
- [Resilient Distributed Parameter Estimation with Heterogeneous Data](https://mlsanity.com/paper/1812.08902/) (2018)
- [Adaptive Diffusion Schemes for Heterogeneous Networks](https://mlsanity.com/paper/1504.01982/) (2015)
- [A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation](https://mlsanity.com/paper/1703.01888/) (2017)
- [Resilient Distributed Field Estimation](https://mlsanity.com/paper/1904.08787/) (2019)
- [Resilient Distributed Diffusion in Networks with Adversaries](https://mlsanity.com/paper/2003.10563/) (2020)

## Abstract (arXiv)

This paper studies distributed diffusion adaptation over clustered multi-task networks in the presence of impulsive interferences and Byzantine attacks. We develop a robust resilient diffusion least mean Geman-McClure-estimation (RDLMG) algorithm based on the cost function used by the Geman-McClure estimator, which can reduce the sensitivity to large outliers and make the algorithm robust under impulsive interferences. Moreover, the mean sub-sequence reduced method, in which each node discards the extreme value information of cost contributions received from its neighbors, can make the network resilient against Byzantine attacks. In this regard, the proposed RDLMG algorithm ensures that all normal nodes converge to their ideal states with cooperation among nodes. A statistical analysis of the RDLMG algorithm is also carried out in terms of mean and mean-square performances. Numerical results evaluate the proposed RDLMG algorithm in applications to multi-target localization and multi-task spectrum sensing.