  To improve federated training of neural networks, we develop FedSparsify, a
sparsification strategy based on progressive weight magnitude pruning. Our
method has several benefits. First, since the size of the network becomes
increasingly smaller, computation and communication costs during training are
reduced. Second, the models are incrementally constrained to a smaller set of
parameters, which facilitates alignment/merging of the local models and
improved learning performance at high sparsification rates. Third, the final
sparsified model is significantly smaller, which improves inference efficiency
and optimizes operations latency during encrypted communication. We show
experimentally that FedSparsify learns a subnetwork of both high sparsity and
learning performance. Our sparse models can reach a tenth of the size of the
original model with the same or better accuracy compared to existing pruning
and nonpruning baselines.
