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This paper introduces a multi-view evidential learning (MVE) method for evaluating the trustworthiness of collaborators in distributed systems by leveraging heterogeneous and uneven quality data from multiple observational views. By modeling each task owner as an independent view and employing the Mamba model for long-sequence trust state analysis, the approach captures the dynamic evolution of trust over time. The incorporation of evidential deep learning allows for quantifying uncertainty in trust assessments, leading to superior accuracy and task success rates compared to existing methods.
Trust evaluations can now adaptively integrate multi-source evidence while quantifying uncertainty, leading to more reliable collaborator selection in distributed systems.
Selection of trustworthy collaborators in distributed systems is critical for efficient task completion, necessitating the inference of trustworthiness from their past collaboration experience. However, as a collaborator serves distinct devices across diverse scenarios in past collaborations, its trust-related data, observed from different device-specific views, is inherently multi-source, heterogeneous, and uneven in quality. Consequently, achieving accurate trust evaluations for collaborator selection remains a major challenge. To tackle these issues, we propose a novel multi-view evidential learning (MVE) based trust evaluation method. First, to accommodate the multi-source heterogeneity of observed trust-related data, we model each task owner who has interacted with a potential collaborator as an independent observational view, enabling the evaluation of the collaborator's view-specific trust. Second, to address the dynamic evolution of trust under changing conditions, we leverage the powerful long-sequence modeling capability of the Mamba model to capture the deep temporal patterns of a collaborator's trust state within each view. Furthermore, to quantify the certainty levels of view-specific trust assessments, we incorporate an evidential deep learning mechanism in MVE, which outputs trust evaluation results while quantifying the subjective uncertainty underlying them. Finally, we employ a dynamic evidential fusion strategy to adaptively integrate the multi-view evidence based on their respective quantified uncertainties, thereby yielding a final trust evaluation for the collaborator. Extensive experiments demonstrate that the proposed MVE method outperforms baselines in both trust evaluation accuracy and task success rate.
