FedSLoP applies stochastic low-rank gradient projections in federated learning to reduce communication volume and client memory while proving O(1/sqrt(NT)) convergence to stationary points under standard assumptions and showing competitive accuracy on heterogeneous MNIST.
Achieving linear speedup with partial worker participation in non-IID federated learning
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FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
FedSLoP applies stochastic low-rank gradient projections in federated learning to reduce communication volume and client memory while proving O(1/sqrt(NT)) convergence to stationary points under standard assumptions and showing competitive accuracy on heterogeneous MNIST.