A federated learning algorithm that trains local stochastic neural networks to capture both the true function and the noise in each client's data.
A Backward SDE Method for Uncertainty Quantification in Deep Learning
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abstract
We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem. An efficient stochastic gradient descent algorithm is introduced under the stochastic maximum principle framework. Numerical experiments for applications of stochastic neural networks are carried out to validate the effectiveness of our methodology.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Federated Learning on Stochastic Neural Networks
A federated learning algorithm that trains local stochastic neural networks to capture both the true function and the noise in each client's data.