optimization error
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
However, due to the heterogeneity between the clients' data distributions, the model obtained through the use of FL algorithms may perform poorly on some client's data.
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Tight Risk Bounds for Gradient Descent on Separable Data
Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.
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Tight Risk Bounds for Gradient Descent on Separable Data
Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.
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Stagewise Training Accelerates Convergence of Testing Error Over SGD
Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang
But how to explain this phenomenon has been largely ignored by existing studies.
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