基于证据深度学习的合作者选择多视图信任评估
Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learning
- Western University（西安大略大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对分布式系统中合作者信任数据多源异构等问题，提出基于MVE的多视图信任评估方法，结合Mamba模型与证据深度学习，实验显示其性能优于基线方法。
AI中文摘要：
分布式系统中，选择可信合作者对高效完成任务至关重要，需从合作者过往协作经验中推断其可信度。但合作者在过往协作中会在不同场景服务不同设备，从特定设备视角观测到的其信任相关数据具有多源、异构且质量不均的特性，因此实现准确的合作者信任评估仍是重大挑战。为解决这些问题，本文提出一种新颖的基于多视图证据学习（MVE）的信任评估方法。首先，为适配观测到的信任相关数据的多源异构性，将与潜在合作者有过交互的每个任务所有者建模为独立观测视图，实现合作者的视图特定信任评估。其次，为应对信任在变化条件下的动态演变，利用强大的长序列建模能力的Mamba模型，捕捉每个视图内合作者信任状态的深层时间模式。此外，为量化视图特定信任评估的确定性水平，在MVE中引入证据深度学习机制，该机制输出信任评估结果的同时量化其 underlying 主观不确定性。最后，采用动态证据融合策略，基于各视图量化的不确定性自适应整合多视图证据，从而得到合作者的最终信任评估。大量实验表明，所提MVE方法在信任评估准确率和任务成功率上均优于基线方法。
英文摘要：
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.
