提出双聚合机制框架DAMe，解决社交事件检测中的数据异构问题
DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism
- 用贝叶斯优化实现局部聚合，融合全局知识并保留本地特征
- 引入全局聚合策略，使客户端获取最大外部偏好知识
- 适用于多语言、跨平台的个性化社交事件检测场景
通过联邦学习（FedSED）训练社交事件检测模型旨在提升参与方在该任务上的表现。然而，现有联邦学习范式难以实现此目标，在处理社交数据固有的异构性方面存在局限。本文提出一种具有双聚合机制的个性化联邦学习框架DAMe，用于社交事件检测。我们提出一种新颖的局部聚合策略，利用贝叶斯优化在融入全局知识的同时保留本地特性；同时引入全局聚合策略，为客户端提供最大外部偏好知识。此外，我们设计了全局-局部事件中心约束，以防止本地过拟合并缓解“客户端漂移”问题。在涵盖六种语言和两个社交媒体平台的六个社交事件数据集上，通过真实联邦环境模拟及消融实验验证了所提框架的有效性。进一步的鲁棒性分析表明，DAMe对注入攻击具有抗性。
原文摘要 · Abstract (English)
Training social event detection models through federated learning (FedSED) aims to improve participants' performance on the task. However, existing federated learning paradigms are inadequate for achieving FedSED's objective and exhibit limitations in handling the inherent heterogeneity in social data. This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. We present a novel local aggregation strategy utilizing Bayesian optimization to incorporate global knowledge while retaining local characteristics. Moreover, we introduce a global aggregation strategy to provide clients with maximum external knowledge of their preferences. In addition, we incorporate a global-local event-centric constraint to prevent local overfitting and ``client-drift''. Experiments within a realistic simulation of a natural federated setting, utilizing six social event datasets spanning six languages and two social media platforms, along with an ablation study, have demonstrated the effectiveness of the proposed framework. Further robustness analyses have shown that DAMe is resistant to injection attacks.
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