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  "extra_authors": [
            
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    {
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      "extra_author": "Shijing Si",
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      "extra_author": "Shijing Si",
            
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    },
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      "extra_author": "Jianzong Wang",
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      "extra_author": "Jianzong Wang",
            
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    },
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      "extra_author": "Yuhan Dong",
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    },
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      "extra_author": "Zhitao Zhu",
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      "extra_author": "Zhitao Zhu",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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      "extra_author": "Jing Xiao",
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      "extra_author": "Jing Xiao",
            
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  "notes": "Federated learning (FL) is a paradigm where many clients 
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  "notes": "Federated learning (FL) is a paradigm where many clients 
            
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collaboratively train a model under the coordination of a central 
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collaboratively train a model under the coordination of a central 
            
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server, while keeping the training data locally stored. However, 
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server, while keeping the training data locally stored. However, 
            
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heterogeneous data distributions over different clients remain a 
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heterogeneous data distributions over different clients remain a 
            
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challenge to mainstream FL algorithms, which may cause slow 
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challenge to mainstream FL algorithms, which may cause slow 
            
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convergence, overall performance degradation and unfairness of 
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convergence, overall performance degradation and unfairness of 
            
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performance across clients.",
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performance across clients.",
            
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  "title": "A Fair Federated Learning Framework With Reinforcement 
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  "title": "A Fair Federated Learning Framework With Reinforcement 
            
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Learning",
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Learning",
            
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