Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

• URL: http://arxiv.org/abs/2603.25334v1
• Date: Thu, 26 Mar 2026 11:21:22 GMT
• Title: Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
• Abstract summary: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices.<n> Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence of faulty or adversarial updates.<n>This paper presents a lightweight agentic trust coordination approach for FL in sustainable and resilient industrial networks.
• Score: 3.7802766761560442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices. Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence of faulty or adversarial updates. Trust based mechanisms are commonly used to mitigate these effects, yet most remain statistical and heuristic, relying on fixed parameters or simple adaptive rules that struggle to accommodate changing operating conditions. This paper presents a lightweight agentic trust coordination approach for FL in sustainable and resilient industrial networks. The proposed Agentic Trust Control Layer operates as a server side control loop that observes trust related and system level signals, interprets their evolution over time, and applies targeted trust adjustments when instability is detected. The approach extends prior adaptive trust mechanisms by enabling context aware intervention decisions, rather than relying on fixed or purely reactive parameter updates. By explicitly separating observation, reasoning, and action, the proposed framework supports stable FL operation without modifying client side training or increasing communication overhead.

Related papers

• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• Accelerating Trust Convergence in IIoT: A ML Approach for Dynamic Network Conditions [5.9556977806856155]
  We propose a dynamic trust management solution, known as the Trust Convergence Acceleration (TCA) approach.<n>Our model predicts the number of time units needed for trust convergence based on key network metrics.<n>We demonstrate the effectiveness of the TCA-based approach, achieving up to a 28.6% reduction in trust convergence time under challenging conditions.
  arXiv  Detail & Related papers  (2026-06-18T13:29:06Z)
• FERA: Uncertainty-Aware Federated Reasoning for Large Language Models [60.52562148874846]
  We study federated reasoning, where a server improves multi-step reasoning by coordinating with heterogeneous clients holding private demonstrations.<n>The key challenge is that client reliability is query-dependent, while the server cannot inspect client data to determine which contributions are trustworthy.<n>We propose Uncertainty-Aware Federated Reasoning (FERA), a training-free framework based on iterative server-client co-refinement.
  arXiv  Detail & Related papers  (2026-05-11T07:04:51Z)
• Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health [3.523596898195567]
  This paper presents a trust aware federated learning framework for interpreting bone healing stages.<n>The framework employs a multi-layer perceptron model trained across simulated clients using the Flower FL framework.
  arXiv  Detail & Related papers  (2026-02-27T11:28:58Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection [0.0]
  Federated Learning (FL) facilitates collaborative model training among distributed clients while ensuring that raw data remains on local devices.<n>Such clients can interfere with the training process by sending misleading updates, which can negatively affect the performance and reliability of the global model.<n>We propose the Federated Learning with Loss Trend Detection (FL-LTD), a lightweight and privacy-preserving defense framework that detects and mitigates malicious behavior by monitoring temporal loss dynamics rather than model gradients.
  arXiv  Detail & Related papers  (2026-01-28T18:09:53Z)
• PaTAS: A Parallel System for Trust Propagation in Neural Networks Using Subjective Logic [1.8485970721272895]
  This paper introduces the Parallel Trust Assessment System (PaTAS), a framework for modeling and propagating trust in neural networks.<n>Experiments on real-world and adversarial datasets demonstrate that PaTAS produces interpretable, symmetric, and convergent trust estimates.
  arXiv  Detail & Related papers  (2025-11-25T18:15:36Z)
• FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning [0.6524460254566904]
  Federated learning (FL) enables collaborative model training while preserving data privacy.<n>It remains vulnerable to malicious clients who compromise model integrity through Byzantine attacks, data poisoning, or adaptive adversarial behaviors.<n>We propose FLARE, an adaptive reputation-based framework that transforms client reliability assessment from binary decisions to a continuous, multi-dimensional trust evaluation.
  arXiv  Detail & Related papers  (2025-11-18T17:57:40Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
  In this paper, we propose a novel reliable federated disentangling network, termed RFedDis. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling. Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
  arXiv  Detail & Related papers  (2023-01-30T11:46:34Z)

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.