PracMHBench evaluates eight model-heterogeneous federated learning algorithms under practical edge device constraints and finds that depth-level heterogeneity wins under compute/communication limits while memory limits change the ranking.
End-to-end evaluation of federated learning and split learning for internet of things
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PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
PracMHBench evaluates eight model-heterogeneous federated learning algorithms under practical edge device constraints and finds that depth-level heterogeneity wins under compute/communication limits while memory limits change the ranking.