Tackling Non-IIDness in HAPS-Aided Federated Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Farajzadeh, Amin Yadav, Animesh Yanikomeroglu, Halim |
| author_facet | Farajzadeh, Amin Yadav, Animesh Yanikomeroglu, Halim |
| contents | High-altitude platform stations (HAPS) enable large-scale federated learning (FL) in non-terrestrial networks (NTN) by providing wide-area coverage and predominantly line-of-sight (LoS) connectivity to many ground users. However, practical deployments face heterogeneous and non-independently and identically distributed (non-IID) client data, which degrades accuracy and slows convergence. We propose a weighted attribute-based client selection strategy that leverages server-side indicators: historical traffic behavior, instantaneous channel quality, computational capability, and prior-round learning contribution. At each round, the HAPS computes a composite score and selects the top clients, while adapting attribute weights online based on their correlation with validation-loss improvement. We further provide theoretical justification that traffic-derived uniformity can serve as a proxy for latent data heterogeneity, enabling selection of client subsets with reduced expected non-IIDness. Simulations demonstrate improved test accuracy, faster convergence, and lower training loss compared with random, resource-only, and single-attribute baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tackling Non-IIDness in HAPS-Aided Federated Learning Farajzadeh, Amin Yadav, Animesh Yanikomeroglu, Halim Networking and Internet Architecture Computer Vision and Pattern Recognition Machine Learning High-altitude platform stations (HAPS) enable large-scale federated learning (FL) in non-terrestrial networks (NTN) by providing wide-area coverage and predominantly line-of-sight (LoS) connectivity to many ground users. However, practical deployments face heterogeneous and non-independently and identically distributed (non-IID) client data, which degrades accuracy and slows convergence. We propose a weighted attribute-based client selection strategy that leverages server-side indicators: historical traffic behavior, instantaneous channel quality, computational capability, and prior-round learning contribution. At each round, the HAPS computes a composite score and selects the top clients, while adapting attribute weights online based on their correlation with validation-loss improvement. We further provide theoretical justification that traffic-derived uniformity can serve as a proxy for latent data heterogeneity, enabling selection of client subsets with reduced expected non-IIDness. Simulations demonstrate improved test accuracy, faster convergence, and lower training loss compared with random, resource-only, and single-attribute baselines. |
| title | Tackling Non-IIDness in HAPS-Aided Federated Learning |
| topic | Networking and Internet Architecture Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.05308 |