Secure Federated Learning: An Evaluation of Homomorphic Encrypted Network Traffic Prediction

Sanon, Sogo Pierre; Reddy, Rekha; Lipps, Christoph; Schotten, Hans D.

doi:10.1109/ccnc51644.2023.10060116

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…For example, Jing Ma et al developed an FL model with multi-key homomorphic encryption using the CKKS scheme [84]. Overall, the CKKS scheme is widely researched, e.g., in FL [39,41,85,86], SL [87,88], and TL [86,89,90].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Distributed Learning in the IoT–Edge–Cloud Continuum

Arzovs,

Judvaitis,

Nesenbergs

et al. 2024

MAKE

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The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.

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