Distributed Rumor Source Detection via Boosted Federated Learning

How to localize the rumor source is an extremely important matter for all sectors of the society. Many researchers have tried to use deep-learning-based graph models to detect rumor sources, but they have neglected how to train their deep-learning-based graph models in the noisy social network environment efficiently. Especially for deep learning models, the performance relies on the data scale. However, even though it is known that a substantial amount of rumor data distributed across multiple edge servers (e.g., cross-platform), due to conflicting business interests, its challenging to coordinate all parties to train a model driven by many samples while avoiding moving data. Federated learning, is an effective technique to bridge this gap. Therefore, this paper proposes a Distributed Rumor Source Detection via Boosted Federated Learning (DRSDBFL). Specifically, this paper proposes an effective rumor source detection method based on a deep-learning-based graph model with a denoising module. To the best of our knowledge, we are the first to attempt the use of a denoising module to reduce the noisy effects of social networks. Then, we propose a novel boosted federated learning mechanism through boosting the high-quality edge worker to improve the training efficiency. Finally, the effectiveness of the proposed method is verified by extensive experiments.