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1. Trust-Aware Task Allocation With Quantum Optimization in Adversarial Multi-Agent Systems

   https://doi.org/10.1109/ICDMW69685.2025.00348  

   Barroso, Giselle Roman; Ho, Duy; Miller, Luke; Alanazi, Ahmed; Lee, Yugyung (November 2025, IEEE)

   This paper introduces a trust-aware task allocation framework for adversarial multi-agent systems, leveraging quantum optimization to improve decision-making under uncertainty. The approach models agent reliability and adversarial behavior through a trust-aware utility function, enabling robust and adaptive task assignment in dynamic environments. By incorporating quantum-inspired optimization techniques, the framework efficiently explores complex solution spaces that are difficult for classical methods. Experimental results demonstrate improved resilience to adversarial agents, enhanced allocation efficiency, and overall system robustness compared to baseline approaches. The proposed framework contributes to secure and scalable coordination in distributed systems, with applications in cybersecurity, autonomous systems, and networked sensing environments. 

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2. MALLM: Multi-Agent Decision-Making with LLMs for Multi-User Edge-Sensor Environments

   https://doi.org/10.1145/3764944.3764947  

   Fu, Heming; Pan, Weici; Zhou, Liangkai; Zhang, Zeyu; Liu, Zhenhua; Lin, Shan (August 2025, ACM SIGMETRICS Performance Evaluation Review)

   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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3. Cognitive swarming: An approach from the theoretical neuroscience of hippocampal function

   https://doi.org/10.1117/12.2518966  

   Monaco, Joseph D.; Hwang, Grace M.; Schultz, Kevin M.; Zhang, Kechen (May 2019, Proceedings of SPIE)

   The rise of mobile multi-agent robotic platforms is outpacing control paradigms for tasks that require operating in complex, realistic environments. To leverage inertial, energetic, and cost bene fits of small-scale robots, critical future applications may depend on coordinating large numbers of agents with minimal onboard sensing and communication resources. In this article, we present the perspective that adaptive and resilient autonomous control of swarms of minimal agents might follow from a direct analogy with the neural circuits of spatial cognition in rodents. We focus on spatial neurons such as place cells found in the hippocampus. Two major emergent hippocampal phenomena, self-stabilizing attractor maps and temporal organization by shared oscillations, reveal theoretical solutions for decentralized self-organization and distributed communication in the brain. We consider that autonomous swarms of minimal agents with low-bandwidth communication are analogous to brain circuits of oscillatory neurons with spike-based propagation of information. The resulting notion of `neural swarm control' has the potential to be scalable, adaptive to dynamic environments, and resilient to communication failures and agent attrition. We illustrate a path toward extending this analogy into multi-agent systems applications and discuss implications for advances in decentralized swarm control. 

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4. Enhancing Smart Home Security: Blockchain-Enabled Federated Learning with Knowledge Distillation for Intrusion Detection

   https://doi.org/10.3390/smartcities8010035  

   Shalan, Mohammed; Hasan, Md Rakibul; Bai, Yan; Li, Juan (March 2025, Smart Cities)

   The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats. 

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5. Private Anomaly Detection in Linear Controllers: Garbled Circuits vs. Homomorphic Encryption

   https://doi.org/10.1109/CDC51059.2022.9992616  

   Alexandru, Andreea B.; Burbano, Luis; Celiktug, Mestan F.; Gomez, Juanita; Cardenas, Alvaro A.; Kantarcioglu, Murat; Katz, Jonathan (December 2022, IEEE)

   Anomaly detection can ensure the operational integrity of control systems by identifying issues such as faulty sensors and false data injection attacks. At the same time, we need privacy to protect personal data and limit the information attackers can get about the operation of a system. However, anomaly detection and privacy can sometimes be at odds, as monitoring the system’s behavior is impeded by data hiding. Cryptographic tools such as garbled circuits and homomorphic encryption can help, but each of these is best suited for certain different types of computation. Control with anomaly detection requires both types of computations so a naive cryptographic implementation might be inefficient. To address these challenges, we propose and implement protocols for privacy-preserving anomaly detection in a linear control system using garbled circuits, homomorphic encryption, and a combination of the two. In doing so, we show how to distribute private computations between the system and the controller to reduce the amount of computation–in particular at the low-power system. Finally, we systematically compare our proposed protocols in terms of precision, computation, and communication costs. 

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