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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2023
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| Online-Zugang: | https://arxiv.org/abs/2312.12091 |
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| _version_ | 1866929572603232256 |
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| author | Fan, Boyu Jiang, Siyang Su, Xiang Tarkoma, Sasu Hui, Pan |
| author_facet | Fan, Boyu Jiang, Siyang Su, Xiang Tarkoma, Sasu Hui, Pan |
| contents | As privacy concerns continue to grow, federated learning (FL) has gained significant attention as a promising privacy-preserving technology, leading to considerable advancements in recent years. Unlike traditional machine learning, which requires central data collection, FL keeps data localized on user devices. However, conventional FL assumes that all clients operate with identical model structures initialized by the server. In real-world applications, system heterogeneity is common, with clients possessing varying computational capabilities. This disparity can hinder training for resource-limited clients and result in inefficient resource use for those with greater processing power. To address this challenge, model-heterogeneous FL has been introduced, enabling clients to train models of varying complexity based on their hardware resources. This paper reviews state-of-the-art approaches in model-heterogeneous FL, analyzing their strengths and weaknesses, while identifying open challenges and future research directions. To the best of our knowledge, this is the first survey to specifically focus on model-heterogeneous FL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12091 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects Fan, Boyu Jiang, Siyang Su, Xiang Tarkoma, Sasu Hui, Pan Distributed, Parallel, and Cluster Computing As privacy concerns continue to grow, federated learning (FL) has gained significant attention as a promising privacy-preserving technology, leading to considerable advancements in recent years. Unlike traditional machine learning, which requires central data collection, FL keeps data localized on user devices. However, conventional FL assumes that all clients operate with identical model structures initialized by the server. In real-world applications, system heterogeneity is common, with clients possessing varying computational capabilities. This disparity can hinder training for resource-limited clients and result in inefficient resource use for those with greater processing power. To address this challenge, model-heterogeneous FL has been introduced, enabling clients to train models of varying complexity based on their hardware resources. This paper reviews state-of-the-art approaches in model-heterogeneous FL, analyzing their strengths and weaknesses, while identifying open challenges and future research directions. To the best of our knowledge, this is the first survey to specifically focus on model-heterogeneous FL. |
| title | A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2312.12091 |