2024/09/01 by Xiaoyan Yu, Yu, Xiaoyan, Yifan Wei +13
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.2409.00614
openalex publication_date 2024/09/01 · openalex created_date 2024/09/29 · openalex updated_date 2026/07/28
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.