SILAGE is a variance-reduced algorithm for nested finite-sum nonconvex optimization that uses O(n) memory, evaluates at most one local group gradient per iteration, and adapts convergence to data heterogeneity parameters δ1 and δ2.
Mime: Mimicking centralized stochastic algorithms in federated learning.arXiv preprint arXiv:2008.03606, 2020a
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FedBCGD reduces communication in federated learning by a factor of 1/N through block-wise parameter updates with accelerated convergence guarantees.
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.
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SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums
SILAGE is a variance-reduced algorithm for nested finite-sum nonconvex optimization that uses O(n) memory, evaluates at most one local group gradient per iteration, and adapts convergence to data heterogeneity parameters δ1 and δ2.
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FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
FedBCGD reduces communication in federated learning by a factor of 1/N through block-wise parameter updates with accelerated convergence guarantees.
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Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.