  Federated learning (FL) is a distributed learning technique that trains a
shared model over distributed data in a privacy-preserving manner.
Unfortunately, FL's performance degrades when there is (i) variability in
client characteristics in terms of computational and memory resources (system
heterogeneity) and (ii) non-IID data distribution across clients (statistical
heterogeneity). For example, slow clients get dropped in FL schemes, such as
Federated Averaging (FedAvg), which not only limits overall learning but also
biases results towards fast clients. We propose FedPrune; a system that tackles
this challenge by pruning the global model for slow clients based on their
device characteristics. By doing so, slow clients can train a small model
quickly and participate in FL which increases test accuracy as well as
fairness. By using insights from Central Limit Theorem, FedPrune incorporates a
new aggregation technique that achieves robust performance over non-IID data.
Experimental evaluation shows that Fed- Prune provides robust convergence and
better fairness compared to Federated Averaging.
