Caesar lowers federated learning communication costs by tailoring model and gradient compression ratios to device staleness and data importance, with only 0.68% accuracy loss.
Billion-scale feder- ated learning on mobile clients: A submodel design with tunable pri- vacy
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Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
Caesar lowers federated learning communication costs by tailoring model and gradient compression ratios to device staleness and data importance, with only 0.68% accuracy loss.