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    To ensure the privacy of processed data, federated learning approaches involve local differential privacy techniques which however require communicating a large amount of data that needs protection. The authors propose here a framework that uses selected small data to transfer knowledge in federated learning with privacy guarantees.

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    • Qi et al. @Tsinghua_Uni @MSFTResearch propose a framework that uses selected small data to transfer #knowledge in #federated #learning with #privacy guarantees #GettingApplied https://t.co/PGJFsuk7mf

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    In the quest to understand how deep neural networks work, identification of slow and fast variables is a desirable step. Inspired by tools from theoretical physics, the authors propose a simplified description of finite deep neural networks based on two matrix variables per layer and provide analytic predictions for feature learning effects.

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    • Inspired by tools from theoretical physics, @Gadi_Naveh_ML and colleagues pinpoint two variables in #DeepNeuralNetworks to quantify feature #learning effects and use those to diagnose and predict performance #GettingApplied https://t.co/e5uWXDXenN