标题： 远离视线，远离思维：图联邦推荐的反距离加权方法 显示英文标题

标题： Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation

摘要： 图联邦推荐系统为传统的集中式推荐架构提供了一种保护隐私的替代方案，后者通常会引发数据安全方面的担忧。 尽管联邦学习能够在不暴露原始用户数据的情况下实现个性化推荐，但现有的聚合方法忽略了在此设置中用户嵌入的独特性质。 事实上，传统聚合方法未能考虑到它们的复杂性以及用户相似性在推荐效果中的关键作用。 此外，不断变化的用户交互需要适应性的聚合方法，同时保留高相关性锚点用户的影响力（在基于图的框架中扩展前的主要用户）。 为了解决这些限制，我们引入了 Dist-FedAvg，这是一种基于距离的新型聚合方法，旨在增强图联邦学习中的个性化和聚合效率。 我们的方法为嵌入相似的用户分配更高的聚合权重，同时确保锚点用户在本地更新中保持显著的影响力。 在多个数据集上的实证评估表明，Dist-FedAvg始终优于基线聚合技术，在提高推荐准确性的同时，保持与现有联邦学习框架的无缝集成。 显示英文摘要

摘要： Graph federated recommendation systems offer a privacy-preserving alternative to traditional centralized recommendation architectures, which often raise concerns about data security. While federated learning enables personalized recommendations without exposing raw user data, existing aggregation methods overlook the unique properties of user embeddings in this setting. Indeed, traditional aggregation methods fail to account for their complexity and the critical role of user similarity in recommendation effectiveness. Moreover, evolving user interactions require adaptive aggregation while preserving the influence of high-relevance anchor users (the primary users before expansion in graph-based frameworks). To address these limitations, we introduce Dist-FedAvg, a novel distance-based aggregation method designed to enhance personalization and aggregation efficiency in graph federated learning. Our method assigns higher aggregation weights to users with similar embeddings, while ensuring that anchor users retain significant influence in local updates. Empirical evaluations on multiple datasets demonstrate that Dist-FedAvg consistently outperforms baseline aggregation techniques, improving recommendation accuracy while maintaining seamless integration into existing federated learning frameworks.