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Adaptive Phase-Switching for Communication-Efficient Federated LoRA Fine-Tuning

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Federated fine-tuning of large language models with low-rank adaptation reduces per-client trainable parameters, but client-to-server communication remains the dominant cost. Existing accounting for federated LoRA protocols omits the asymmetric transition round when a protocol changes aggregation mode, and reports savings that ignore grouped-query attention shapes. This paper measures per-round upload and download bytes for a bidirectional B-only federated LoRA protocol and places five methods, three from prior work, on a single communication-quality frontier. The frontier has a knee, which an

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