FedSLoP reduces communication and memory costs in federated learning through stochastic low-rank gradient projections, with a nonconvex convergence rate of O(1/sqrt(NT)) and competitive accuracy on heterogeneous MNIST data.
A geometric convergence theory for the preconditioned steepest descent iteration.SIAM Journal on Numerical Analysis, 50(6):3188–3207
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FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
FedSLoP reduces communication and memory costs in federated learning through stochastic low-rank gradient projections, with a nonconvex convergence rate of O(1/sqrt(NT)) and competitive accuracy on heterogeneous MNIST data.