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  "access_rights": "",
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  "author": "Bingzhe Wu",
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  "author": "Bingzhe Wu",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "defined_in": "https://doi.org/10.48550/arXiv.2204.07742",
            
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  "doi": "10.57702/yz6rbzpo",
            
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  "doi_date_published": "2024-12-03",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Zhipeng Liang",
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      "extra_author": "Zhipeng Liang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Yuxuan Han",
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      "extra_author": "Yuxuan Han",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Yatao Bian",
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      "extra_author": "Yatao Bian",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Peilin Zhao",
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      "extra_author": "Peilin Zhao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Junzhou Huang",
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      "extra_author": "Junzhou Huang",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "name": "mixup-techniques",
            
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      "title": "Mixup Techniques"
            
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  "name": 
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  "name": 
            
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ly-robust-federated-learning-with-inter-client-noise-via-local-mixup",
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ly-robust-federated-learning-with-inter-client-noise-via-local-mixup",
            
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  "notes": "The authors propose a general framework to solve the two 
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  "notes": "The authors propose a general framework to solve the two 
            
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challenges simultaneously: inter-client data heterogeneity and 
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challenges simultaneously: inter-client data heterogeneity and 
            
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intra-client data noise. The framework uses distributionally robust 
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intra-client data noise. The framework uses distributionally robust 
            
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optimization to mitigate the negative effects caused by data 
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optimization to mitigate the negative effects caused by data 
            
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heterogeneity and incorporates mixup techniques into the local 
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heterogeneity and incorporates mixup techniques into the local 
            
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training process to mitigate the effects of intra-client data noise.",
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training process to mitigate the effects of intra-client data noise.",
            
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      "id": "af36c7e1-240c-47d9-bc72-43b9e7142e9f",
            
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      "name": "Intra-client Data Noise",
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      "name": "Intra-client Data Noise",
            
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      "display_name": "Mixup Techniques",
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      "display_name": "Mixup Techniques",
            
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      "id": "30b3af74-6cd7-4578-9d6a-55ffe75b96b1",
            
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      "name": "Mixup Techniques",
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    }
            
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  ],
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  ],
            
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  "title": "DRFLM: Distributionally Robust Federated Learning with 
175
  "title": "DRFLM: Distributionally Robust Federated Learning with 
            
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Inter-client Noise via Local Mixup",
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Inter-client Noise via Local Mixup",
            
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  "version": ""
            
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}
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}