Simultrain Solution File

of SimulTrain is that the forward pass of one batch and the backward pass of a previous batch can overlap in time, if we carefully manage parameter versions and gradients. This is analogous to CPU pipelining but applied to distributed training across heterogeneous compute nodes.

[ w^(e) \leftarrow \beta w^(e) + (1-\beta) w^(c) ] simultrain solution

where ( T_\textsend ) and ( T_\textrecv ) depend on bandwidth, and ( T_\textforward, T_\textbackward ) on model size. For large models (e.g., ResNet-50), ( T_\textsend \gg T_\textforward ) on typical 4G/5G networks. of SimulTrain is that the forward pass of

SimulTrain sends activations (lower dimension than raw data but higher than gradients). However, it enables bidirectional overlap , reducing total bandwidth-time product by 65% compared to SyncSGD. | Dataset | Centralized | SyncSGD | FedAvg (5 local steps) | SimulTrain | |-------------|-------------|---------|------------------------|------------| | UCF-101 | 84.2% | 83.9% | 81.1% | 83.7% | | WISDM | 91.5% | 91.3% | 88.9% | 91.1% | For large models (e

Proof sketch: The forecast term cancels first-order bias from staleness. Weight reconciliation prevents error accumulation. The pipeline yields the same effective gradient steps per unit time. Hardware: Edge = Raspberry Pi 4 (4GB RAM), Cloud = AWS g4dn.xlarge (NVIDIA T4). Network: emulated 4G (50 Mbps, 30 ms RTT) and 5G (300 Mbps, 10 ms RTT).