Does Perplexity Predict Protection? Backbone Selection for Worst-Client Fairness in Federated Parameter-Efficient Fine-Tuning

Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poorly served even when the group’s average accuracy looks fine. We ask whether the choice of pretrained backbone affects this under LoRA fine-tuning, and whether per-word perplexity on the target text is associated with which backbone helps the worst-off client before federated training starts. We ran 313 experiments across three text-classification datasets and three similarly sized backbones (RoBERTa, BERTweet, PubMedBERT), each compared against a task-specific baseline on identical data splits. Lower-perplexity backbones were associated with larger gains for the worst-performing client (rank correlation −0.87 across nine dataset–backbone pairs; block-permutation p=0.028, the smallest value this design can produce). This is an exploratory finding: a backbone held out of the analysis was directionally consistent with it, but that is one prospective check, not a validation. The same ordering predicts mean-client accuracy equally well, so the correlation does not isolate a worst-client-specific effect; a cross-client dispersion measure is also associated with perplexity, but no result in this family survives multiplicity correction. Ditto, at the settings and skew we tested, recovered only 4–12% of the gap between training alone and full federation; other personalization methods were not tested. Removing aggregation erased the benefit, and a client’s update was close to orthogonal to the group’s update, which is inconsistent with one proposed explanation as we operationalized it. Practically, per-word perplexity on a sample of task text is a provisional shortlisting signal for backbone choice. We release our code, predictions, and full results for others to test.