发表机构
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 中文总结
本研究针对可信合作者选择的长期行为评估挑战，提出双向Mamba模型BM，通过构建图聚合特征并整合时间依赖，提升评估准确率以选出高价值合作者。
AI 中文摘要
有效选择可信合作者对于确保协作任务成功完成至关重要，这需要准确评估设备的长期行为和短期协作动态。从历史协作中学习到的一致设备行为模式可用于预测设备在未来协作中的可靠性，但基于历史协作准确评估设备行为仍存在挑战：第一，基于有限历史协作的行为评估仅能捕捉瞬时过去行为，无法代表设备的真实行为；第二，由于设备行为存在时间依赖关系，仅依赖早期协作的单向评估会错失从后续协作中学习的机会。解决这些挑战需要基于长期协作评估设备行为，同时考虑前向和后向时间依赖关系。为此，本研究提出了一种基于双向Mamba的模型（BM）用于长期行为评估：对于每个短时间槽，基于历史协作在设备间构建图，随后聚合该时间槽内的设备行为特征；接着，双向Mamba模型整合所有时间区间的短期表示，为每个设备生成稳定可靠的长期行为评估。实验结果表明，BM相比基线方法实现了更高的评估准确率，从而能够选择可最大化任务完成价值的合作者。
英文摘要
Effective selection of trustworthy collaborators is crucial to ensuring the successful completion of collaborative tasks, which requires accurate assessments of both long-term device behavior and short-term collaborative dynamics. Consistent device behavior patterns, which are learned from historical collaborations, can be used to predict their reliability in future collaborations. However, accurately assessing device behavior based on historical collaborations remains challenging. First, behavior assessment from limited historical collaborations captures only instantaneous past behavior, failing to represent the devices' true behavior. Second, due to the temporal dependencies of device behavior, a unidirectional evaluation that relies only on earlier collaborations loses the opportunity to learn from subsequent collaborations. Addressing these challenges requires evaluating device behavior based on long-term collaborations while considering both forward and backward temporal dependencies. To this end, this work proposes a bidirectional Mamba-enabled model (BM) for long-term behavioral evaluation. For each short time slot, a graph is constructed among devices based on historical collaborations, and device behavioral features within the slot are then aggregated accordingly. Subsequently, a bidirectional Mamba model integrates these short-term representations across all time intervals, producing a stable and reliable long-term behavior evaluation for each device. Experimental results demonstrate that BM achieves higher evaluation accuracy than baseline methods, thereby enabling the selection of collaborators that maximize the value of task completion.
Journal refIEEE ICNC 2026
