Title: Harnessing Client Drift with Decoupled Gradient Dissimilarity. Abstract: The performance of Federated learning (FL) typically suffers from client drift caused by heterogeneous data, where data distributions vary with clients. Recent studies show that the gradient dissimilarity between clients induced by the data distribution discrepancy causes the client drift. Thus, existing methods mainly focus on correcting the gradients. However, it is challenging to identify which client should (or not) be corrected. This challenge raises a series of questions: will the local training, without gradient correction, contribute to the server model's generalization of other clients' distributions? when the generalization contribution holds? how to address the challenge when it fails? To answer these questions, we analyze the generalization contribution of local training and conclude that the generalization contribution of local training is bounded by the conditional Wasserstein distance between clients' distributions. Thus, the key to promote generalization contribution is to leverage similar conditional distributions for local training. As collecting data distribution can cause privacy leakage, we propose decoupling the deep models, i.e., splitting into high-level models and low-level models, for harnessing client drift. Namely, high-level models are trained on shared feature distributions, causing promoted generalization contribution and alleviated gradient dissimilarity. Experimental results demonstrate that FL with decoupled gradient dissimilarity is robust to data heterogeneity.