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| Title | Toward An Experimental Federated 6G Testbed: A Federated Leaning Approach |
|---|---|
| ID_Doc | 57646 |
| Authors | Kholidy H.A.; Hariri S. |
| Year | 2022 |
| Published | Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 2022-December |
| DOI | http://dx.doi.org/10.1109/AICCSA56895.2022.10017506 |
| Abstract | With the development of the smart systems such as smart city, smart buildings, smart industries, the need for a highly reliable, scalable, and secure communications using high-data-rate and low latency networks such as 6G networks is increased. The artificial intelligence and machine learning (AI/ML) will be pervasive and of key relevance across the security technology stack and architecture in the 6G networks. Despite the advantages of the 6G networks, sophisticated cyberattacks can disrupt the operation of 6G critical infrastructures and associated services. In this paper, we present a novel methodology to create a federated cyber testbed as a service (FCTaaS) that can be offered as a ubiquitous cloud service. Due to the widespread usage of Machine Learning (ML) in critical decision processes of 5G/6G resource management and their applications, there is an exponential growth in cyberattacks to maliciously manipulate the ML algorithms and consequently influence their decision process in favor of the attackers. Currently, there are many isolated cyber testbeds; however, little research has focused on methods to automatically build a federated cyber testbed in general and especially in 6G testbeds. In this paper, we show how to use the FCTaaS services can be used to seamlessly compose a federated cyber testbed that allows researchers to experiment with and evaluate different algorithms to implement different algorithms to conduct data analytics, cybersecurity, and resilient algorithms. In particular, we will show how the FCTaaS can be used to develop highly efficient and accurate federated learning algorithms that can tolerate a wide range of attacks against ML algorithms such as data poisoning and ML model attacks. © 2022 IEEE. |
| Author Keywords | 5I6G; Blockchain; federated cyber testbed; federated learning; resource federation; security; testbed |
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