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.
Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.