Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures

• URL: http://arxiv.org/abs/2607.18359v1
• Date: Mon, 20 Jul 2026 12:43:54 GMT
• Title: Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
• Abstract summary: Decentralized multi-agent reinforcement learning (MARL) should be understood as a distributed alternative to centralized training with decentralized execution.<n>Credit assignment and communication are two central conditions for its practical feasibility.<n>Overall, this paper reframes decentralized MARL as a promising but conditional foundation for resilient critical infrastructures.
• Score: 1.5469452301122175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control. This paper argues that decentralized multi-agent reinforcement learning (MARL) should be understood not merely as a distributed alternative to centralized training with decentralized execution but as a paradigm structurally aligned with the requirements of resilient critical infrastructures. This perspective is grounded in an analysis of the properties of decentralized MARL and the requirements of critical infrastructures, including scalability to large numbers of agents, support for privacy and local autonomy, robustness to failures, and interaction-driven adaptation among interdependent components. However, structural alignment alone is insufficient for practical deployment. This paper identifies credit assignment and communication as two central conditions for its practical feasibility. Credit assignment determines whether local learning remains aligned with system-level objectives, while communication determines whether coordination can be learned and maintained under realistic operational constraints. Building on these challenges, this paper proposes a research agenda focused on structure-aware, causality-aware, and resilience-aware credit assignment; communication for both coordination and credit assignment; and safe, timely, and recoverable decentralized learning under deployment constraints. Overall, this paper reframes decentralized MARL as a promising but conditional foundation for resilient critical infrastructures.

Related papers

• Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks [40.96834401764948]
  6G networks operate across a dynamic, multi-domain edge-cloud continuum.<n>Security serves as the primary enabler of this decentralized paradigm.<n>This paper argues that decentralization, trustworthiness, explainability, and sustainability must be designed jointly.
  arXiv  Detail & Related papers  (2026-09-12T10:52:53Z)
• Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing [54.4933359656657]
  This work proposes an Edge-AI-driven decentralized task allocation framework based on ranking-aware negotiation.<n>A ranking-aware formulation finally reshapes the learning objective to align with the ordering-based nature of winner selection.<n>Results show improved delay and deadline adherence under high load, and enhanced energy efficiency under tighter constraints.
  arXiv  Detail & Related papers  (2026-05-14T20:38:25Z)
• Sovereign Agents: Towards Infrastructural Sovereignty and Diffused Accountability in Decentralized AI [1.5755923640031846]
  We propose infrastructural sovereignty as an analytic lens for understanding how cryptographic self-custody, decentralized execution environments, and protocol-mediated continuity scaffold agentic sovereignty.<n>We argue that sovereignty in such systems exists on a spectrum determined by infrastructural hardness-the degree to which underlying technical systems resist intervention or collapse.
  arXiv  Detail & Related papers  (2026-02-16T17:30:17Z)
• Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation [71.86087908416255]
  We introduce a payoff allocation framework based on the least core (LC) concept.<n>Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction.<n>Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances.
  arXiv  Detail & Related papers  (2026-02-03T11:10:50Z)
• Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks [63.541114376141735]
  Large language models (LLMs) are proliferating rapidly at the edge, delivering intelligent capabilities across diverse application scenarios.<n>However, their practical deployment in collaborative scenarios confronts fundamental challenges: privacy vulnerabilities, communication overhead, and computational bottlenecks.<n>We propose Federated Attention (FedAttn), which integrates the federated paradigm into the self-attention mechanism.
  arXiv  Detail & Related papers  (2025-11-04T15:14:58Z)
• Protocol Learning, Decentralized Frontier Risk and the No-Off Problem [56.74434512241989]
  We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants.<n>This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity.<n>It also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics.
  arXiv  Detail & Related papers  (2024-12-10T19:53:50Z)
• InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management [1.0499611180329806]
  InfraLib is an open-source modular framework for modeling and analyzing infrastructure management problems.<n>It implements hierarchical, deterioration models, supports realistic partial observability, and handles practical constraints.<n>InfraLib provides standardized environments for benchmarking decision-making approaches, along with tools for expert data collection and policy evaluation.
  arXiv  Detail & Related papers  (2024-09-05T01:54:29Z)
• Exploring the Robustness of Decentralized Training for Large Language Models [51.41850749014054]
  Decentralized training of large language models has emerged as an effective way to democratize this technology. This paper explores the robustness of decentralized training from three main perspectives.
  arXiv  Detail & Related papers  (2023-12-01T04:04:03Z)
• The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks [6.661122374160369]
  As Federated Learning (FL) grows in popularity, new decentralized frameworks are becoming widespread. This paper analyzes the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network. We propose a novel attack algorithm that prioritizes adversarial spread over adversarial centrality by maximizing the average network distance between adversaries.
  arXiv  Detail & Related papers  (2023-11-14T06:48:50Z)
• Networked Communication for Decentralised Agents in Mean-Field Games [59.01527054553122]
  We introduce networked communication to the mean-field game framework.<n>We prove that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases.<n>We show that our networked approach has significant advantages over both alternatives in terms of robustness to update failures and to changes in population size.
  arXiv  Detail & Related papers  (2023-06-05T10:45:39Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.