Assessing the Sustainability and Trustworthiness of Federated Learning Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866918133052211200 |
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| author | Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Zumtaugwald, Lynn Bovet, Gerome Stiller, Burkhard |
| author_facet | Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Zumtaugwald, Lynn Bovet, Gerome Stiller, Burkhard |
| contents | Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only enhances data protection but also holds significant promise for intelligent network management, including distributed monitoring, adaptive control, and edge intelligence. Although the trustworthiness of FL systems has received growing attention, the sustainability dimension remains insufficiently explored, despite its importance for scalable real-world deployment. To address this gap, this work introduces sustainability as a distinct pillar within a comprehensive trustworthy FL taxonomy, consistent with AI-HLEG guidelines. This pillar includes three key aspects: hardware efficiency, federation complexity, and the carbon intensity of energy sources. Experiments using the FederatedScope framework under diverse scenarios, including varying participants, system complexity, hardware, and energy configurations, validate the practicality of the approach. Results show that incorporating sustainability into FL evaluation supports environmentally responsible deployment, enabling more efficient, adaptive, and trustworthy network services and management AI models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20435 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Assessing the Sustainability and Trustworthiness of Federated Learning Models Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Zumtaugwald, Lynn Bovet, Gerome Stiller, Burkhard Computers and Society Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only enhances data protection but also holds significant promise for intelligent network management, including distributed monitoring, adaptive control, and edge intelligence. Although the trustworthiness of FL systems has received growing attention, the sustainability dimension remains insufficiently explored, despite its importance for scalable real-world deployment. To address this gap, this work introduces sustainability as a distinct pillar within a comprehensive trustworthy FL taxonomy, consistent with AI-HLEG guidelines. This pillar includes three key aspects: hardware efficiency, federation complexity, and the carbon intensity of energy sources. Experiments using the FederatedScope framework under diverse scenarios, including varying participants, system complexity, hardware, and energy configurations, validate the practicality of the approach. Results show that incorporating sustainability into FL evaluation supports environmentally responsible deployment, enabling more efficient, adaptive, and trustworthy network services and management AI models. |
| title | Assessing the Sustainability and Trustworthiness of Federated Learning Models |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2310.20435 |