A Measured Communication-Quality Frontier for Federated LoRA Fine-Tuning with Adaptive Phase-Switching
Federated fine-tuning of large language models with low-rank adaptation (LoRA) reduces the number of trainable parameters, but communication remains the dominant cost, and protocols are usually compared by parameter-count ratios rather than by measured bytes. This paper measures per-round upload and download bytes for five federated LoRA protocols, three of them from prior work, and places them on a single communication-quality frontier scored by held-out instruction-following loss. The frontier has a knee. ReverseAdaptive, which learns both LoRA factors before freezing one once the relative improvement in training loss falls below a dimensionless threshold, sits at that knee: it cuts mea...
| Authors | Jerry Adams Franklin |
|---|---|
| Primary category | cs.LG |
| Published | 11 September 2026 |
| Last updated at source | 3 October 2026 |
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