  Social media has become an effective platform to generate and spread fake
news that can mislead people and even distort public opinion. Centralized
methods for fake news detection, however, cannot effectively protect user
privacy during the process of centralized data collection for training models.
Moreover, it cannot fully involve user feedback in the loop of learning
detection models for further enhancing fake news detection. To overcome these
challenges, this paper proposed a novel decentralized method, Human-in-the-loop
Based Swarm Learning (HBSL), to integrate user feedback into the loop of
learning and inference for recognizing fake news without violating user privacy
in a decentralized manner. It consists of distributed nodes that are able to
independently learn and detect fake news on local data. Furthermore, detection
models trained on these nodes can be enhanced through decentralized model
merging. Experimental results demonstrate that the proposed method outperforms
the state-of-the-art decentralized method in regard of detecting fake news on a
benchmark dataset.
