FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

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classification accuracy compared to state-of-art FL frameworks when the communication overhead of the local model update transmission is less than 0.1 bit per local model entry. [6/6 of https://t.co/KepsgQUEVd]

error, the convergence rate of the proposed framework is also analyzed for a smooth loss function. Simulation results on the MNIST and CIFAR-10 datasets demonstrate that the proposed framework provides more than a 2.5% increase in [5/6 of https://t.co/K…

of the vector quantizer and the key parameters for the compression are optimized so as to minimize the reconstruction error of the global model update under the constraint of wireless link capacity. By considering the reconstruction [4/6 of https://t.co…