  Federated learning protects data privacy and security by exchanging models
instead of data. However, unbalanced data distributions among participating
clients compromise the accuracy and convergence speed of federated learning
algorithms. To alleviate this problem, unlike previous studies that limit the
distance of updates for local models, we propose global-update-guided federated
learning (FedGG), which introduces a model-cosine loss into local objective
functions, so that local models can fit local data distributions under the
guidance of update directions of global models. Furthermore, considering that
the update direction of a global model is informative in the early stage of
training, we propose adaptive loss weights based on the update distances of
local models. Numerical simulations show that, compared with other advanced
algorithms, FedGG has a significant improvement on model convergence accuracies
and speeds. Additionally, compared with traditional fixed loss weights,
adaptive loss weights enable our algorithm to be more stable and easier to
implement in practice.
