  Federated learning (FL) enables distributed learning across edge devices
while protecting data privacy. However, the learning accuracy decreases due to
the heterogeneity of devices' data, and the computation and communication
latency increase when updating large-scale learning models on devices with
limited computational capability and wireless resources. We consider a FL
framework with partial model pruning and personalization to overcome these
challenges. This framework splits the learning model into a global part with
model pruning shared with all devices to learn data representations and a
personalized part to be fine-tuned for a specific device, which adapts the
model size during FL to reduce both computation and communication latency and
increases the learning accuracy for devices with non-independent and
identically distributed data. The computation and communication latency and
convergence of the proposed FL framework are mathematically analyzed. To
maximize the convergence rate and guarantee learning accuracy, Karush Kuhn
Tucker (KKT) conditions are deployed to jointly optimize the pruning ratio and
bandwidth allocation. Finally, experimental results demonstrate that the
proposed FL framework achieves a remarkable reduction of approximately 50
percent computation and communication latency compared with FL with partial
model personalization.
