When Does Personalization Help in Federated LoRA Fine-Tuning?

Low-rank adaptation (LoRA) makes fine-tuning foundation models inexpensive. Pairing LoRA with federated learning lets clients adapt collaboratively without sharing data. The aim is increasingly a model personalized to each client, but how much personalization helps depends on how clients differ in the amount of local data, label skew, feature shift, and adapter rank. Existing federated LoRA methods each handle only some of these four axes. We propose O4A (All for One, One for All), a single aggregation rule for personalized federated LoRA. O4A distills a shared consensus across clients while preserving each client's personalization. Across the four axes on CIFAR-100 and DomainNet, O4A supports clients at different ranks and improves over existing baselines where personalization matters, giving a regime map of when personalization helps in federated LoRA.