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.
FedCanon: Non-convex composite federated learning with efficient proximal operation on heterogeneous data.IEEE Transactions on Signal Processing, 74:215–229, 2025
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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.