# FedVP

Federated learning usually requires institutions to share one model backbone so parameter updates can be aggregated, which keeps centers running different model families or sizes from collaborating. FedVP moves collaboration from parameter aggregation to shared verification: each client trains a verifier on its local data and candidate responses from its own frozen LLM, and the server pools these independently trained verifiers to score and select answers, never merging backbone parameters. Across five medical question-answering datasets, with centers using Qwen and LLaMA backbones from 3B to 14B parameters, one verifier pool is reused for all five backbones without retraining and raises mean accuracy by 1.46 to 5.79 percentage points over the strongest conventional federated baselines.

Federated learning usually requires institutions to share one model backbone so parameter updates can be aggregated, which keeps centers running different model families or sizes from collaborating. FedVP moves collaboration from parameter aggregation to shared verification: each client trains a verifier on its local data and candidate responses from its own frozen LLM, and the server pools these independently trained verifiers to score and select answers, never merging backbone parameters. Across five medical question-answering datasets, with centers using Qwen and LLaMA backbones from 3B to 14B parameters, one verifier pool is reused for all five backbones without retraining and raises mean accuracy by 1.46 to 5.79 percentage points over the strongest conventional federated baselines.