This is a paper written by Bimal Ghimire and Danda B.Rawat that talks about the recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. Federated cybersecurity (FC) is a revolutionary concept to make the Internet of Things (IoT) safer and more efficient in the future. It has the potential of detecting security threats, taking countermeasures, and limiting the spreading of threats over the IoT network system efficiently. Federated learning (FL) is a privacy-aware ML model that is particularly useful to secure the vulnerable IoT environment. This article discusses the background and comparison of centralized learning, distributed on-site learning, and FL, and a survey of the application of FL to cybersecurity for IoT. It also discusses several approaches that address performance issues associated with FL, which may impact the security and overall performance of the IoT. To anticipate the future evolution of this new paradigm, it discusses the main ongoing research efforts, challenges, and research trends in this areas.
References:
Ghimire, B. and Rawat, D. B. (2022) ‘Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things’, IEEE internet of things journal, 9(11), pp. 8229–8249. doi: 10.1109/jiot.2022.3150363.
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