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{"key"=>"Marulli2024297", "type"=>"Book chapter", "bibtex"=>"@article{Marulli2024297,\n author = {Marulli, Fiammetta and Campanile, Lelio and Marrone, Stefano and Verde, Laura},\n title = {Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study},\n year = {2024},\n journal = {Lecture Notes on Data Engineering and Communications Technologies},\n volume = {203},\n pages = {297 – 306},\n doi = {10.1007/978-3-031-57931-8_29}\n}\n", "author"=>"Marulli, Fiammetta and Campanile, Lelio and Marrone, Stefano and Verde, Laura", "author_array"=>[{"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Stefano", "last"=>"Marrone", "prefix"=>"", "suffix"=>""}, {"first"=>"Laura", "last"=>"Verde", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Fiammetta", "author_0_last"=>"Marulli", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Lelio", "author_1_last"=>"Campanile", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Stefano", "author_2_last"=>"Marrone", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Laura", "author_3_last"=>"Verde", "author_3_prefix"=>"", "author_3_suffix"=>"", "title"=>"Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study", "year"=>"2024", "journal"=>"Lecture Notes on Data Engineering and Communications Technologies", "volume"=>"203", "pages"=>"297 – 306", "doi"=>"10.1007/978-3-031-57931-8_29", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191307171&doi=10.1007%2f978-3-031-57931-8_29&partnerID=40&md5=0b97f4f89a38c5e7006c4e90d6e57b4a", "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.", "author_keywords"=>"Bayesian Ensemble Learning; Cloud Computing; Data Privacy; Edge Computing; Federated Learning", "keywords"=>"Bayesian networks; Data transfer; Edge computing; Internet of things; Learning systems; Bayesian; Bayesian ensemble learning; Cloud environments; Cloud-computing; Distributed environments; Edge computing; Ensemble learning; Exploratory studies; Federated learning; Modern machines; Data privacy", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 0"}