Distributed interference cancellation in multi-agent scenarios

TL;DR

This paper introduces an adaptive distributed algorithm for interference cancellation in multi-agent networks, improving robustness against noisy or malicious nodes by adjusting sharing weights based on agent behavior.

Contribution

It proposes a novel adaptive algorithm for weight sharing in distributed networks, enhancing detection and mitigation of impaired nodes in multi-agent systems.

Findings

Demonstrates effectiveness in multi-agent RL scenarios

Shows improved detection of noisy or malicious nodes

Validates generality across different diffusion algorithms

Abstract

This paper considers the problem of detecting impaired and noisy nodes over network. In a distributed algorithm, lots of processing units are incorporating and communicating with each other to reach a global goal. Due to each one's state in the shared environment, they can help the other nodes or mislead them (due to noise or a deliberate attempt). Previous works mainly focused on proper locating agents and weight assignment based on initial environment state to minimize malfunctioning of noisy nodes. We propose an algorithm to be able to adapt sharing weights according to behavior of the agents. Applying the introduced algorithm to a multi-agent RL scenario and the well-known diffusion LMS demonstrates its capability and generality.