Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. The research community from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection and mitigation of fake news, however it remains challenging to detect and prevent the dissemination of fake news in practice. With AI powered systems, its highly crucial to understand the detector's decision of fake news by means of proper user-friendly explanations when it comes to social media. Hence, in this paper, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present potential future research agenda specially incorporating AI explainable Fake news credibility system.

The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user's data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user's data on-device and exchanges the parameters without sharing the user's data. FL is implemented to secure the user's data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.

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