FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
This addresses privacy and communication issues for distributed learning across devices, but it is incremental as it builds on existing sketching techniques.
The paper tackles communication complexity and privacy challenges in Federated Learning by introducing FedSKETCH and FedSKETCHGATE algorithms, which compress local gradients using count sketch to achieve privacy and communication efficiency, with sharp convergence guarantees and experimental validation.
Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices. In this work, we introduce FedSKETCH and FedSKETCHGATE algorithms to address both challenges in Federated learning jointly, where these algorithms are intended to be used for homogeneous and heterogeneous data distribution settings respectively. The key idea is to compress the accumulation of local gradients using count sketch, therefore, the server does not have access to the gradients themselves which provides privacy. Furthermore, due to the lower dimension of sketching used, our method exhibits communication-efficiency property as well. We provide, for the aforementioned schemes, sharp convergence guarantees. Finally, we back up our theory with various set of experiments.