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Communication-Efficient Distributed SGD with Error-Feedback, Revisited
We show that the convergence proof of a recent algorithm called dist-EF-SGD for distributed stochastic gradient descent with communication efficiency using error-feedback of Zheng et al. (NeurIPS 2019) is problematic mathematically.
On the Convergence Proof of AMSGrad and a New Version
This is also the neglected issue in the convergence proof of Adam.
Privacy-Preserving Deep Learning via Weight Transmission
This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns.
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