  A key challenge in federated learning (FL) is the statistical heterogeneity
that impairs the generalization of the global model on each client. To address
this, we propose a method Federated learning with Adaptive Local Aggregation
(FedALA) by capturing the desired information in the global model for client
models in personalized FL. The key component of FedALA is an Adaptive Local
Aggregation (ALA) module, which can adaptively aggregate the downloaded global
model and local model towards the local objective on each client to initialize
the local model before training in each iteration. To evaluate the
effectiveness of FedALA, we conduct extensive experiments with five benchmark
datasets in computer vision and natural language processing domains. FedALA
outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy.
Furthermore, we also apply ALA module to other federated learning methods and
achieve up to 24.19% improvement in test accuracy.
