An ILP model and BCD heuristic jointly optimize model splitting, node placement, and smashed-data routing in an SFC-based multi-hop split learning/inference architecture to minimize end-to-end latency.
Dynamic Topology and Resource Allocation for Distributed Training in Mobile Edge Computing,
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Optimization of Model Splitting, Placement, and Chaining for Multi-hop Split Learning and Inference
An ILP model and BCD heuristic jointly optimize model splitting, node placement, and smashed-data routing in an SFC-based multi-hop split learning/inference architecture to minimize end-to-end latency.