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Convergence Analysis of Split Federated Learning on Heterogeneous Data
We further extend the analysis to non-convex objectives and where some clients may be unavailable during training.
When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation
SplitFed learning (SFL) is a recent distributed approach that alleviates computation workload at the client device by splitting the model at a cut layer into two parts, where clients only need to train part of the model.
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