In this paper, we intent to handle this emerging issue.

The assumption that data are independently and identically distributed (IID) is staple in statistical machine learning. It suggests that a hypothesis selected by an algorithm, after observing several training samples, should perform effectively on test samples from the same unknown distribution.

Due to space constraints we only address major concerns; all suggestions will be included in the final version. Experimentally we've observed that when using previous weighted We will compare and cite related work (gTop-k) in the final draft. In sec.3 we assume min. SGD has a small critical batch size to approximate a full gradient descent iteration, no matter the size of dataset. Appendix-F shows ScaleCom's scalability in system performance; more Analogously, we perform filtering on the residual gradients (see eq.(5)) Connection will be discussed in the revised version.