FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation
Summary: arXiv:2604.10678v1 Announce Type: new
Abstract
Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are largely developed in isolation, overlooking the benefits of leveraging shared detection patterns across platforms to improve performance and promptly identify emerging bot variants. The heterogeneity of data distributions and model architectures further complicates the design of an effective cross-platform and cross-model detection framework.
Introduction
To address these challenges, researchers have proposed a novel approach known as FedRio, which stands for Personalized Federated Social Bot Detection with Cooperative Reinforced Contrastive Adversarial Distillation. This innovative framework seeks to enhance social bot detection capabilities by effectively utilizing knowledge from diverse sources while maintaining privacy and security across platforms.
Key Components of FedRio
FedRio introduces several key components that collectively enhance the detection of social bots:
- Adaptive Message-Passing Module: This module serves as the backbone for each client, leveraging graph neural networks to facilitate communication and feature sharing among clients.
- Federated Knowledge Extraction Mechanism: Designed based on generative adversarial networks, this mechanism allows for efficient sharing of global data distributions without compromising individual client privacy.
- Multi-Stage Adversarial Contrastive Learning Strategy: This strategy enforces feature space consistency among clients, reducing divergence between local and global models to improve overall detection accuracy.
- Adaptive Server-Side Parameter Aggregation: This component adapts parameter aggregation methods to better accommodate data heterogeneity, ensuring that the model remains robust across different platforms.
- Reinforcement Learning-Based Client-Side Parameter Control: This approach allows for dynamic adjustments based on the unique characteristics of each client’s data, further enhancing the detection process.
Performance Evaluation
Extensive experiments conducted on two real-world social bot detection benchmarks have demonstrated that FedRio consistently outperforms state-of-the-art federated learning baselines in several key areas:
- Detection Accuracy: FedRio shows significant improvements in identifying social bots compared to existing frameworks.
- Communication Efficiency: The framework minimizes the communication overhead typically associated with federated learning.
- Feature Space Consistency: The multi-stage learning strategy ensures that feature representations remain consistent across different clients.
- Privacy Constraints: FedRio operates effectively under stronger privacy constraints while remaining competitive with centralized results.
Conclusion
FedRio represents a significant advancement in the field of social bot detection by promoting collaboration across platforms while respecting user privacy. Its innovative design and effective implementation pave the way for more robust and efficient detection mechanisms that can adapt to the ever-evolving landscape of online social interactions. As social media platforms continue to grapple with the challenges posed by bots, solutions like FedRio will be essential in ensuring the integrity and security of online communities.