Quantized and Distributed Subgradient Optimization Method with Malicious Attack

TL;DR

This paper introduces a quantized, distributed subgradient optimization method resilient to malicious agents, ensuring convergence to near-optimal solutions despite adversarial behavior and communication constraints.

Contribution

It proposes a novel distributed gradient algorithm that handles malicious agents and quantized communication, with proven convergence properties.

Findings

Algorithm converges to a neighborhood of the optimal solution.

Effectiveness demonstrated through numerical simulations.

Handles malicious agents and communication quantization constraints.

Abstract

This paper considers a distributed optimization problem in a multi-agent system where a fraction of the agents act in an adversarial manner. Specifically, the malicious agents steer the network of agents away from the optimal solution by sending false information to their neighbors and consume significant bandwidth in the communication process. We propose a distributed gradient-based optimization algorithm in which the non-malicious agents exchange quantized information with one another. We prove convergence of the solution to a neighborhood of the optimal solution, and characterize the solutions obtained under the communication-constrained environment and presence of malicious agents. Numerical simulations to illustrate the results are also presented.