Matrix von Neumann entropy of final-layer gradients acts as a data-free proxy for client contribution in federated learning, showing high correlation with standalone accuracy on non-IID benchmarks.
A new approach to linear filtering and prediction problems.Transactions of the ASME–Journal of Basic Engineering, 82(Series D):35–45
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Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy
Matrix von Neumann entropy of final-layer gradients acts as a data-free proxy for client contribution in federated learning, showing high correlation with standalone accuracy on non-IID benchmarks.