Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
Data-centric artificial intelligence: A survey,
2 Pith papers cite this work. Polarity classification is still indexing.
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Higher face density causes monotonic performance degradation in models and acts as a domain shift, even under balanced sampling.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
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Face Density as a Proxy for Data Complexity: Quantifying the Hardness of Instance Count
Higher face density causes monotonic performance degradation in models and acts as a domain shift, even under balanced sampling.