# Resilient Distributed Optimization Algorithms for Resource Allocation

## TL;DR

This paper introduces a resilient distributed optimization algorithm that maintains convergence despite Byzantine attacks on communication channels, enhancing the security and robustness of resource allocation in cyber-physical systems.

## Contribution

It develops a robust primal-dual algorithm incorporating advanced statistics to counteract Byzantine attacks in distributed resource allocation.

## Key findings

- Algorithm converges to a neighborhood of the robust model
- Neighborhood size is proportional to attack fraction
- Enhances security in distributed resource management

## Abstract

Distributed algorithms provide flexibility over centralized algorithms for resource allocation problems, e.g., cyber-physical systems. However, the distributed nature of these algorithms often makes the systems susceptible to man-in-the-middle attacks, especially when messages are transmitted between price-taking agents and a central coordinator. We propose a resilient strategy for distributed algorithms under the framework of primal-dual distributed optimization. We formulate a robust optimization model that accounts for Byzantine attacks on the communication channels between agents and coordinator. We propose a resilient primal-dual algorithm using state-of-the-art robust statistics methods. The proposed algorithm is shown to converge to a neighborhood of the robust optimization model, where the neighborhood's radius is proportional to the fraction of attacked channels.