Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learning
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 heterogenei...
| Authors | Botao Zhu, Xianbin Wang |
|---|---|
| Primary category | eess.SY |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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