Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios
The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL traini...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/6/342 |
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| author | Athanasios Psaltis Kassiani Zafeirouli Peter Leškovský Stavroula Bourou Juan Camilo Vásquez-Correa Aitor García-Pablos Santiago Cerezo Sánchez Anastasios Dimou Charalampos Z. Patrikakis Petros Daras |
| author_facet | Athanasios Psaltis Kassiani Zafeirouli Peter Leškovský Stavroula Bourou Juan Camilo Vásquez-Correa Aitor García-Pablos Santiago Cerezo Sánchez Anastasios Dimou Charalampos Z. Patrikakis Petros Daras |
| author_sort | Athanasios Psaltis |
| collection | DOAJ |
| description | The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process. |
| first_indexed | 2024-03-11T02:20:12Z |
| format | Article |
| id | doaj.art-38e93b300ad5467d8b02391d19d161be |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T02:20:12Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-38e93b300ad5467d8b02391d19d161be2023-11-18T10:54:45ZengMDPI AGInformation2078-24892023-06-0114634210.3390/info14060342Fostering Trustworthiness of Federated Learning Ecosystem through Realistic ScenariosAthanasios Psaltis0Kassiani Zafeirouli1Peter Leškovský2Stavroula Bourou3Juan Camilo Vásquez-Correa4Aitor García-Pablos5Santiago Cerezo Sánchez6Anastasios Dimou7Charalampos Z. Patrikakis8Petros Daras9Centre for Research and Technology Hellas, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, 57001 Thessaloniki, GreeceVicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, SpainSynelixis Solutions S.A., 34100 Chalkida, GreeceVicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, SpainVicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, SpainVicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, SpainCentre for Research and Technology Hellas, 57001 Thessaloniki, GreeceDepartment of Electrical and Electronics Engineering, University of West Attica, 12241 Athens, GreeceCentre for Research and Technology Hellas, 57001 Thessaloniki, GreeceThe present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.https://www.mdpi.com/2078-2489/14/6/342federated learningtrustworthinessprivacy-preserving technologies |
| spellingShingle | Athanasios Psaltis Kassiani Zafeirouli Peter Leškovský Stavroula Bourou Juan Camilo Vásquez-Correa Aitor García-Pablos Santiago Cerezo Sánchez Anastasios Dimou Charalampos Z. Patrikakis Petros Daras Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios Information federated learning trustworthiness privacy-preserving technologies |
| title | Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios |
| title_full | Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios |
| title_fullStr | Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios |
| title_full_unstemmed | Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios |
| title_short | Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios |
| title_sort | fostering trustworthiness of federated learning ecosystem through realistic scenarios |
| topic | federated learning trustworthiness privacy-preserving technologies |
| url | https://www.mdpi.com/2078-2489/14/6/342 |
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