  Federated learning (FL) is a privacy-preserving paradigm for collaboratively
training a global model from decentralized clients. However, the performance of
FL is hindered by non-independent and identically distributed (non-IID) data
and device heterogeneity. In this work, we revisit this key challenge through
the lens of gradient conflicts on the server side. Specifically, we first
investigate the gradient conflict phenomenon among multiple clients and reveal
that stronger heterogeneity leads to more severe gradient conflicts. To tackle
this issue, we propose FedGH, a simple yet effective method that mitigates
local drifts through Gradient Harmonization. This technique projects one
gradient vector onto the orthogonal plane of the other within conflicting
client pairs. Extensive experiments demonstrate that FedGH consistently
enhances multiple state-of-the-art FL baselines across diverse benchmarks and
non-IID scenarios. Notably, FedGH yields more significant improvements in
scenarios with stronger heterogeneity. As a plug-and-play module, FedGH can be
seamlessly integrated into any FL framework without requiring hyperparameter
tuning.
