A federated learning scheme that compresses client gradients with truncated SVD or Tucker decomposition and then quantizes the components, cutting transmitted bits by about 90 to 97 percent at some accuracy cost.
Communication complexity of distributed convex learning and optimization,
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Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications
A federated learning scheme that compresses client gradients with truncated SVD or Tucker decomposition and then quantizes the components, cutting transmitted bits by about 90 to 97 percent at some accuracy cost.