Sustainable federated learning with mobile crowdsensing: a DRL approach for learning efficiency maximization

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dc.contributor.authorZhao, Jiahuien
dc.contributor.authorChen, Mingen
dc.contributor.authorLe, Maien
dc.contributor.authorHuang, Mengyanen
dc.contributor.authorYang, Zhaohuien
dc.contributor.authorPham, Quoc-Vieten
dc.contributor.funderResearch and development plan projects of Jiangsu Provinceen
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2025-11-04T11:58:57Z
dc.date.available2025-11-04T11:58:57Z
dc.date.issued2025-09-26en
dc.description.abstractIn this paper, we propose a Sustainable Sensing Federated Learning (S2FL) system where Internet-of-Things (IoT) devices and mobile users harvest energy wirelessly to perform data sensing, local Federated Learning (FL) training, and model update transmissions. We formulate a joint optimization problem that considers transmission power, CPU frequency, bandwidth allocation, and time allocation to maximize long-term learning efficiency. To solve this complex and dynamic problem, we develop an algorithm that integrates Deep Reinforcement Learning (DRL) with optimization techniques, leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm for time allocation and a Lagrangian-Based Block Coordinate Descent (BCD) Method for per-slot resource optimization. Simulation results demonstrate that our proposed DDPG-based DRL-S2FL algorithm significantly outperforms benchmark schemes, achieving up to 15 % higher average reward compared to Deep Q-Networks (DQN) and 40 % greater performance than Random strategies. This work highlights the effectiveness of combining advanced optimization techniques with deep reinforcement learning to enhance federated learning performance in dynamic wireless environments.en
dc.description.sponsorshipResearch and development plan projects of Jiangsu Province (Grant no. BE2022316)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationZhao, J., Chen, M., Le, M., Huang, M., Yang, Z. and Pham, Q.-V. (2025) ‘Sustainable federated learning with mobile crowdsensing: a DRL approach for learning efficiency maximization’, 2025 IEEE International Conference on Communications, Montreal, QC, Canada, 08-12 June 2025, pp. 3833–3838. https://doi.org/10.1109/ICC52391.2025.11161520en
dc.identifier.doi10.1109/ICC52391.2025.11161520en
dc.identifier.eissn1938-1883en
dc.identifier.endpage3838en
dc.identifier.isbn979-8-3315-0521-9en
dc.identifier.issn1550-3607en
dc.identifier.startpage3833en
dc.identifier.urihttps://hdl.handle.net/10468/18145
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HE::HORIZON-TMA-MSCA-SE/101182933/EU/Evolution in Security and Privacy for 6G networks/ENSURE-6Gen
dc.relation.urihttps://ieeexplore.ieee.org/xpl/conhome/11160703/proceedingen
dc.rights© 2025, IEEE. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectFederated learningen
dc.subjectMobile Crowdsensingen
dc.subjectEnergy harvestingen
dc.subjectReinforcement learningen
dc.titleSustainable federated learning with mobile crowdsensing: a DRL approach for learning efficiency maximizationen
dc.typeConference itemen
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