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Social media has become an effective platform to generate and spread fakenews that can mislead people and even distort public opinion. Centralizedmethods for fake news detection, however, cannot effectively protect userprivacy during the process of centralized data collection for training models.Moreover, it cannot fully involve user feedback in the loop of learningdetection models for further enhancing fake news detection. To overcome thesechallenges, this paper proposed a novel decentralized method, Human-in-the-loopBased Swarm Learning (HBSL), to integrate user feedback into the loop oflearning and inference for recognizing fake news without violating user privacyin a decentralized manner. It consists of distributed nodes that are able toindependently learn and detect fake news on local data. Furthermore, detectionmodels trained on these nodes can be enhanced through decentralized modelmerging. Experimental results demonstrate that the proposed method outperformsthe state-of-the-art decentralized method in regard of detecting fake news on abenchmark dataset.

Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection