  This work considers the category distribution heterogeneity in federated
learning. This issue is due to biased labeling preferences at multiple clients
and is a typical setting of data heterogeneity. To alleviate this issue, most
previous works consider either regularizing local models or fine-tuning the
global model, while they ignore the adjustment of aggregation weights and
simply assign weights based on the dataset size. However, based on our
empirical observations and theoretical analysis, we find that the dataset size
is not optimal and the discrepancy between local and global category
distributions could be a beneficial and complementary indicator for determining
aggregation weights. We thus propose a novel aggregation method, Federated
Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation
weights not only involve both the dataset size and the discrepancy value, but
also contribute to a tighter theoretical upper bound of the optimization error.
FedDisco also promotes privacy-preservation, communication and computation
efficiency, as well as modularity. Extensive experiments show that our FedDisco
outperforms several state-of-the-art methods and can be easily incorporated
with many existing methods to further enhance the performance. Our code will be
available at https://github.com/MediaBrain-SJTU/FedDisco.
