FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation

2026-04-12

AI Summary

This work addresses the challenge of knowledge sharing in cross-platform social bot detection, which is hindered by data heterogeneity, model divergence, and privacy constraints. To overcome these issues, the authors propose a personalized federated learning framework that integrates an adaptive message-passing graph neural network, federated generative adversarial knowledge distillation, multi-stage adversarial contrastive learning, and a reinforcement learning–driven mechanism for client-side parameter modulation and server-side adaptive aggregation. The framework significantly enhances cross-platform feature consistency, communication efficiency, and detection accuracy while preserving data privacy. Experimental results on two real-world datasets demonstrate that the proposed method outperforms existing federated learning approaches and achieves performance comparable to centralized models.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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. To address these challenges, we propose FedRio (Personalized Federated Social Bot Detection with Cooperative Reinforced Contrastive Adversarial Distillation framework. We first introduce an adaptive message-passing module as the graph neural network backbone for each client. To facilitate efficient knowledge sharing of global data distributions, we design a federated knowledge extraction mechanism based on generative adversarial networks. Additionally, we employ a multi-stage adversarial contrastive learning strategy to enforce feature space consistency among clients and reduce divergence between local and global models. Finally, we adopt adaptive server-side parameter aggregation and reinforcement learning-based client-side parameter control to better accommodate data heterogeneity in heterogeneous federated settings. Extensive experiments on two real-world social bot detection benchmarks demonstrate that FedRio consistently outperforms state-of-the-art federated learning baselines in detection accuracy, communication efficiency, and feature space consistency, while remaining competitive with published centralized results under substantially stronger privacy constraints.

Problem

• social bot detection
• federated learning
• data heterogeneity
• cross-platform detection
• model heterogeneity

Innovation

• Federated Learning
• Adversarial Contrastive Learning
• Graph Neural Networks
• Reinforcement Learning
• Generative Adversarial Networks