Venue & metadata

• Journal/Proceedings: Lecture Notes on Data Engineering and Communications Technologies
• Volume: 203
• Pages: 297 – 306
• Note: Cited by: 0
• Author keywords: Bayesian Ensemble Learning; Cloud Computing; Data Privacy; Edge Computing; Federated Learning

Abstract

Conventional modern Machine Learning (ML) applications involve training models in the cloud and then transferring them back to the edge, especially in an Internet of Things (IoT) enabled environment. However, privacy-related limitations on data transfer from the edge to the cloud raise challenges: among various solutions, Federated Learning (FL) could satisfy privacy related concerns and accommodate power and energy issues of edge devices. This paper proposes a novel approach that combines FL and Ensemble Learning (EL) to improve both security and privacy challenges. The presented methodology introduces an extra layer, the Federation Layer, to enhance security. It uses Bayesian Networks (BNs) to dynamically filter untrusted/unsecure federation clients. This approach presents a solution for increasing the security and robustness of FL systems, considering also privacy and performance aspects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Bayesian networks GS Data transfer GS Edge computing GS Internet of things GS Learning systems GS Bayesian GS Bayesian ensemble learning GS Cloud environments GS Cloud-computing GS Distributed environments GS Edge computing GS Ensemble learning GS Exploratory studies GS Federated learning GS Modern machines GS Data privacy GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Marulli, F., Campanile, L., Marrone, S., & Verde, L. (2024). Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study [Book chapter]. Lecture Notes on Data Engineering and Communications Technologies, 203, 297–306. https://doi.org/10.1007/978-3-031-57931-8_29