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Federated learning accuracy jumps by up to 7% simply by using a multi-task autoencoder to identify and filter out noisy or uninformative samples on each client.
Laplacian DP and adaptive quantization can slash federated learning communication costs by over 50% without sacrificing accuracy or privacy, even with non-IID data.
