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In the field of multi-agent autonomous transportation, such as automated payload delivery or highway on-ramp merging, agents routinely exchange knowledge to optimize their shared objective and adapt to environmental novelties through Cooperative Multi-Agent Reinforcement Learning (CMARL) algorithms. This knowledge exchange between agents allows these systems to operate efficiently and adapt to dynamic environments. However, this cooperative learning process is susceptible to adversarial poisoning attacks, as highlighted by contemporary research. Particularly, the poisoning attacks where malicious agents inject deceptive information camouflaged within the differential noise, a pivotal element for differential privacy (DP)-based CMARL algorithms, pose formidable challenges to identify and overcome. The consequences of not addressing this issue are far-reaching, potentially jeopardizing safety-critical operations and the integrity of data privacy in these applications. Existing research has strived to develop anomaly detection-based defense models to counteract conventional poisoning methods. Nonetheless, the recurring necessity for model offloading and retraining with labeled anomalous data undermines their practicality, considering the inherently dynamic nature of the safety-critical autonomous transportation applications. Further, it is imperative to maintain data privacy, ensure high performance, and adapt to environmental changes. Motivated by these challenges, this paper introduces a novel defense mechanism against stealthy adversarial poisoning attacks in the autonomous transportation domain, termed Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation (RAMPART). Leveraging a GAN model at each local node, RAMPART effectively filters out malicious advice in an unsupervised manner, whilst generating synthetic samples for each state-action pair to accommodate environmental uncertainties and eliminate the need for labeled training data. Our extensive experimental analysis, conducted in a Private Payload Delivery Network (PPDN) —a common application in the autonomous multi-agent transportation domain—demonstrates thatRAMPART successfully defends against a DP-exploited poisoning attack with a\(30\% \)attack ratio, achieving an F1 score of 0.852 and accuracy of\(96.3\% \)in heavy-traffic environments.
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This content will become publicly available on August 26, 2026
MALLM: Multi-Agent Decision-Making with LLMs for Multi-User Edge-Sensor Environments
Multi-user environments present significant challenges in coordinating diverse preferences and resolving conflicts around shared resources. Current systems use a single-agent approach that struggles to balance individual needs with collective objectives. We introduce MALLM, a novel framework that deploys personalized LLM-based agents for each user on edge devices. MALLM integrates multi-sensor data fusion with a structured multi-agent decision-making mechanism, processing all data locally for enhanced privacy. Our edge-computing architecture enables real-time deliberation through evidence-based argumentation and consensus formation algorithms. The system continuously refines user profiles through sensor data while managing computational resources e!ciently. We evaluate MALLM through two case studies-health monitoring and personalized comfort management- demonstrating improved conflict resolution and resource e!ciency compared to conventional approaches. Our results show that MALLM e''ectively balances competing user priorities while preserving privacy in complex shared environments.
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- PAR ID:
- 10668939
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM SIGMETRICS Performance Evaluation Review
- Volume:
- 53
- Issue:
- 2
- ISSN:
- 0163-5999
- Page Range / eLocation ID:
- 3 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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