  Federated learning (FL) has prevailed as an efficient and privacy-preserved
scheme for distributed learning. In this work, we mainly focus on the
optimization of computation and communication in FL from a view of pruning. By
adopting layer-wise pruning in local training and federated updating, we
formulate an explicit FL pruning framework, FedLP (Federated Layer-wise
Pruning), which is model-agnostic and universal for different types of deep
learning models. Two specific schemes of FedLP are designed for scenarios with
homogeneous local models and heterogeneous ones. Both theoretical and
experimental evaluations are developed to verify that FedLP relieves the system
bottlenecks of communication and computation with marginal performance decay.
To the best of our knowledge, FedLP is the first framework that formally
introduces the layer-wise pruning into FL. Within the scope of federated
learning, more variants and combinations can be further designed based on
FedLP.
