  As a promising approach to deal with distributed data, Federated Learning
(FL) achieves major advancements in recent years. FL enables collaborative
model training by exploiting the raw data dispersed in multiple edge devices.
However, the data is generally non-independent and identically distributed,
i.e., statistical heterogeneity, and the edge devices significantly differ in
terms of both computation and communication capacity, i.e., system
heterogeneity. The statistical heterogeneity leads to severe accuracy
degradation while the system heterogeneity significantly prolongs the training
process. In order to address the heterogeneity issue, we propose an
Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two
novel methods. First, we propose an asynchronous FL system model with a
dynamical model aggregation method between updated local models and the global
model on the server for superior accuracy and high efficiency. Then, we propose
an adaptive local model adjustment method by aggregating the fresh global model
with local models on devices to further improve the accuracy. Extensive
experimentation with 6 models and 5 public datasets demonstrates that FedASMU
significantly outperforms baseline approaches in terms of accuracy (0.60% to
23.90% higher) and efficiency (3.54% to 97.98% faster).

Distributed, Parallel, and Cluster Computing