Contribution-Aware Bandwidth Allocation for Multimodal Split Learning
Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, because a client holding several sensor streams cannot host an encoder per modality. Split Learning makes such training feasible by keeping only the first layers on the device, at the cost of an uplink that must carry smashed activations for every modality at every step. Existing compression schemes give each modality the same keep-ratio, so the shared budget is divided in proportion to smashed-activation dimension, a quantity unrelated to how much each modality contributes to the fused prediction. We make that division an explicit decision and ...
| Authors | Iason Ofeidis, Leandros Tassiulas |
|---|---|
| Primary category | cs.LG |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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