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.
Linear convergence in federated learning: Tackling client heterogeneity and sparse gradi- ents,
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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.