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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.21698 |
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| _version_ | 1866908470992699392 |
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| author | Salama, Abdelaziz Qazzaz, Mohammed M. H. Shah, Syed Danial Ali Hafeez, Maryam Zaidi, Syed Ali Ahmadi, Hamed |
| author_facet | Salama, Abdelaziz Qazzaz, Mohammed M. H. Shah, Syed Danial Ali Hafeez, Maryam Zaidi, Syed Ali Ahmadi, Hamed |
| contents | Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication efficiency and ensure robust FL operations. EcoFL implements a two-stage optimisation approach: an RL-based rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions. Experimental results demonstrate competitive FL model performance with 19\% lower power consumption compared to baseline approaches, highlighting substantial potential for scalable, energy-efficient collaborative learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks Salama, Abdelaziz Qazzaz, Mohammed M. H. Shah, Syed Danial Ali Hafeez, Maryam Zaidi, Syed Ali Ahmadi, Hamed Signal Processing Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication efficiency and ensure robust FL operations. EcoFL implements a two-stage optimisation approach: an RL-based rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions. Experimental results demonstrate competitive FL model performance with 19\% lower power consumption compared to baseline approaches, highlighting substantial potential for scalable, energy-efficient collaborative learning applications. |
| title | EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.21698 |