FedCensus: Partition-Invariant Federated Learning from Additive Sufficient Statistics

Federated learning trains a shared model across clients while keeping their data local. With weight averaging, the model drifts when client data is non-IID, and the accuracy is bounded by the lowest-computation client. Methods that share prototypes, logits, or synthetic data share only the knowledge from each client's own dataset. We present FedCensus, a federated model with a fine and a coarse codebook learned inside the federation, three additive feature statistics computed against them, and a closed-form classifier, so every parameter is computed from additive client statistics. Because additive statistics do not depend on how the data is split, the trained model is the same under every partition and every number of clients. Because the sums cover every class, each client's model achieves nearly the same accuracy on classes absent from its local dataset as on those present. A client with a lower training budget uploads its statistics in installments and chooses its deployment level after training, so the accuracy of the deployed model is not bound by the lowest-computation client. On four datasets, FedCensus exceeds the state-of-the-art computation-heterogeneous, prototype-based, and logit-based methods. On CIFAR-100, FedCensus is comparable to the best homogeneous method at matched deployment FLOPs. FedCensus reaches this accuracy in 18 communication rounds and reduces the communication overhead by 60% compared with FedAvg. The complete source code is available at https://anonymous.4open.science/r/fedcensus-79E5/.