Heterogeneous Wireless Federated Learning Framework via Over-the-Air Computation
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Le résumé fourni par la source
The vision of sixth-generation networks, powered by artificial intelligence (AI) and edge computing, is to establish a ubiquitous and immersive wireless communication infrastructure that facilitates the transition to the advanced Artificial Intelligence of Things (AIoT) paradigm. To realize this vision, wireless federated learning (WFL) has emerged as a promising approach for distributing AI computations across diverse endpoints while ensuring data privacy within the wireless network. As the exploration of access entities expands, AIoT systems encounter challenges due to the multi-heterogeneity of devices, including diverse computation capabilities and data distribution. To address these challenges, we propose a novel synchronous/asynchronous WFL mechanism with over-the-air computation, which simultaneously ensures time efficiency, communication efficiency, and performance accuracy. Specifically, this framework leverages both global and local momentum to accelerate convergence. Then, a masking matrix is introduced to mitigate heavy noise, balance communication overhead with training loss, and reduce the mean squared error in model aggregation after optimal beamforming design. To further improve performance in such heterogeneous settings, a fair-weighted aggregation method is employed to tackle the biased device selection problem in asynchronous aggregation, especially in the non-independent and identically distributed (i.i.d.) case. Finally, the proposed framework is validated by both i.i.d. and non-i.i.d. MNIST datasets, with extensive numerical results demonstrating fast convergence and improved aggregation performance under varying computational diversity.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Heterogeneous Wireless Federated Learning Framework via Over-the-Air Computation
- Date Crossref
- 01/02/2026
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Southwest Jiaotong University Provincial Key Laboratory of Information Coding and Transmission pays non établi dans la noticeUniversité ou école supérieure
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Southwest University pays non établi dans la noticeUniversité ou école supérieure
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Aristotle University of Thessaloniki Department of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Ocean University of China pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Science pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Technology pays non établi dans la noticeUniversité ou école supérieure
Provincial Key Laboratory of Information Coding and Transmission — Southwest Jiaotong University, Southwest University et Department of Electrical and Computer Engineering — Aristotle University of Thessaloniki, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.