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Communication complexity and privacy are the two key challenges in FederatedLearning where the goal is to perform a distributed learning through a largevolume of devices. In this work, we introduce FedSKETCH and FedSKETCHGATEalgorithms to address both challenges in Federated learning jointly, wherethese algorithms are intended to be used for homogeneous and heterogeneous datadistribution settings respectively. The key idea is to compress theaccumulation of local gradients using count sketch, therefore, the server doesnot have access to the gradients themselves which provides privacy.Furthermore, due to the lower dimension of sketching used, our method exhibitscommunication-efficiency property as well. We provide, for the aforementionedschemes, sharp convergence guarantees.  Finally, we back up our theory with various set of experiments.

FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching