FedSub achieves O(rd) uplink communication and reduced gradient memory with a nonconvex convergence bound that includes a residual error floor from random projection variance.
Assumption 2.Each functionf i(x)isL f -smooth such that ∥∇fi(x)− ∇fi(z)∥≤L f ∥x−z∥,∀i, x, z
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An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
FedSub achieves O(rd) uplink communication and reduced gradient memory with a nonconvex convergence bound that includes a residual error floor from random projection variance.