An incentive-aware federated bargaining approach for client selection in decentralized federated learning for IoT smart homes
Abstract Federated Learning (FL) has emerged as a promising solution for privacy-preserving model training across distributed IoT devices. Despite its advantages, FL faces challenges such as inefficient client selection, data heterogeneity, security vulnerabilities, and exposure to Man-in-the-Middle...
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| מחבר ראשי: | |
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| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
Nature Portfolio
2025-10-01
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| סדרה: | Scientific Reports |
| נושאים: | |
| גישה מקוונת: | https://doi.org/10.1038/s41598-025-17407-1 |
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