Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916837046878208 |
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| author | Domini, Davide Erhan, Laura Aguzzi, Gianluca Cavallaro, Lucia Zenoozi, Amirhossein Douzandeh Liotta, Antonio Viroli, Mirko |
| author_facet | Domini, Davide Erhan, Laura Aguzzi, Gianluca Cavallaro, Lucia Zenoozi, Amirhossein Douzandeh Liotta, Antonio Viroli, Mirko |
| contents | Federated Learning offers privacy-preserving collaborative intelligence but struggles to meet the sustainability demands of emerging IoT ecosystems necessary for Society 5.0-a human-centered technological future balancing social advancement with environmental responsibility. The excessive communication bandwidth and computational resources required by traditional FL approaches make them environmentally unsustainable at scale, creating a fundamental conflict with green AI principles as billions of resource-constrained devices attempt to participate. To this end, we introduce Sparse Proximity-based Self-Federated Learning (SParSeFuL), a resource-aware approach that bridges this gap by combining aggregate computing for self-organization with neural network sparsification to reduce energy and bandwidth consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0 Domini, Davide Erhan, Laura Aguzzi, Gianluca Cavallaro, Lucia Zenoozi, Amirhossein Douzandeh Liotta, Antonio Viroli, Mirko Machine Learning Federated Learning offers privacy-preserving collaborative intelligence but struggles to meet the sustainability demands of emerging IoT ecosystems necessary for Society 5.0-a human-centered technological future balancing social advancement with environmental responsibility. The excessive communication bandwidth and computational resources required by traditional FL approaches make them environmentally unsustainable at scale, creating a fundamental conflict with green AI principles as billions of resource-constrained devices attempt to participate. To this end, we introduce Sparse Proximity-based Self-Federated Learning (SParSeFuL), a resource-aware approach that bridges this gap by combining aggregate computing for self-organization with neural network sparsification to reduce energy and bandwidth consumption. |
| title | Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.07613 |