Differentially Private Malicious Agent Avoidance in Multiagent Advising Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Cybersecurity

Background:

• Agent advising enhances multi-agent learning but faces challenges from malicious agents and communication constraints.
• Existing methods often overlook the impact of non-cooperative agents and communication overhead.

Purpose of the Study:

• To propose a novel differentially private agent advising approach to address limitations in current methods.
• To mitigate the effects of malicious agents providing false advice.
• To manage communication overhead in constrained environments.

Main Methods:

• The proposed approach utilizes differential privacy, specifically the Laplace mechanism, to add noise to agent rewards.
• This noise injection aids student agents in selecting reliable teacher agents.
• The privacy budget concept is employed to control communication overhead.

Main Results:

• The differentially private approach effectively reduces the impact of malicious agents without explicit identification.
• The method successfully controls communication overhead, a critical factor in restricted environments.
• Experimental results validate the proposed approach's effectiveness.

Conclusions:

• Differentially private agent advising offers a robust solution for improving agent learning in the presence of adversarial behavior.
• The approach provides a practical mechanism for managing communication costs in real-world multi-agent systems.