HASFL jointly optimizes per-device batch sizes and neural network split points to reduce training latency in heterogeneous split federated learning, guided by a new convergence bound.
Optimiz- ing Parameter Mixing Under Constrained Communications in Parallel Federated Learning,
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HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
HASFL jointly optimizes per-device batch sizes and neural network split points to reduce training latency in heterogeneous split federated learning, guided by a new convergence bound.