A lightweight federated split learning scheme combining client-side pruning, gradient quantization, and activation dropout achieves comparable or better CIFAR-10 accuracy with lower communication, but the convergence theorem omits dropout and has proof gaps.
FedSL: Federated split learning for collaborative healthcare analytics on resource-constrained wearable iomt devices,
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Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks
A lightweight federated split learning scheme combining client-side pruning, gradient quantization, and activation dropout achieves comparable or better CIFAR-10 accuracy with lower communication, but the convergence theorem omits dropout and has proof gaps.