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  "access_rights": "",
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  "author": "Aleksei Ponomarenko-Timofeev",
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  "author": "Aleksei Ponomarenko-Timofeev",
            
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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.2303.10254",
            
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  "doi": "10.57702/ye0h42cx",
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  "doi": "10.57702/ye0h42cx",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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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": "Olga Galinina",
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      "extra_author": "Olga Galinina",
            
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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": "Ravikumar Balakrishnan",
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      "extra_author": "Ravikumar Balakrishnan",
            
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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": "Nageen Himayat",
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      "extra_author": "Nageen Himayat",
            
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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": "Sergey Andreev",
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      "extra_author": "Sergey Andreev",
            
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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": "Yevgeni Koucheryavy",
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      "extra_author": "Yevgeni Koucheryavy",
            
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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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      "description": "",
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      "name": "federated-learning",
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      "name": "federated-learning",
            
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      "title": "Federated Learning"
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      "title": "Federated Learning"
            
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      "description": "",
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      "name": "multi-task-learning",
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      "name": "multi-task-learning",
            
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      "title": "Multi-task Learning"
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      "title": "Multi-task Learning"
            
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  "name": 
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"multi-task-learning-for-federated-classification-and-regression",
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"multi-task-learning-for-federated-classification-and-regression",
            
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  "notes": "The proposed algorithm allows personalizing the learning 
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  "notes": "The proposed algorithm allows personalizing the learning 
            
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model for each participant without sharing the training data and 
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model for each participant without sharing the training data and 
            
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improves the performance, compared to that of the locally trained 
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improves the performance, compared to that of the locally trained 
            
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models provide. The method is especially bene\ufb01cial in the case of 
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models provide. The method is especially bene\ufb01cial in the case of 
            
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the low volumes of data available to individual participants.",
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the low volumes of data available to individual participants.",
            
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      "display_name": "multi-task learning",
            
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      "id": "bcad4df5-310a-41e2-b58a-634318066212",
            
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      "name": "multi-task learning",
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      "name": "multi-task learning",
            
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      "state": "active",
            
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      "vocabulary_id": null
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    },
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    {
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    {
            
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      "display_name": "regression",
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      "display_name": "regression",
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Multi-Task Learning for Federated Classification and 
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  "title": "Multi-Task Learning for Federated Classification and 
            
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Regression",
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Regression",
            
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}