Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing

Abstract:We propose PRISM-FCP (Partial shaRing and robust calIbration with Statistical Margins for Federated Conformal Prediction), a communication-efficient Byzantine-robust federated conformal prediction framework that uses partial model sharing to mitigate stochastic model-poisoning attacks during training and histogram-based filtering to mitigate adversarial calibration submissions. Existing robust FCP approaches primarily address adversarial behavior during calibration, leaving training-stage poisoning to separate robust-training mechanisms. PRISM-FCP instead considers the coupling between the two stages. During training, clients partially share updates by transmitting only $M$ of $D$ parameters per round. Under the stated stochastic attack model, this attenuates the expected energy of each Byzantine client's perturbation contribution to the aggregate by a factor of $M/D$ relative to full sharing. When this benefit outweighs the optimization slowdown caused by partial updates, it can reduce training error and improve interval efficiency. During calibration, the server uses client-provided characterization vectors to filter suspected Byzantine clients before estimating the conformal quantile from the retained clients. Experiments on synthetic benchmarks and the UCI Superconductivity and YearPredictionMSD datasets, including Gaussian, ALIE, and sign-flipping training-stage attacks, demonstrate near-nominal empirical coverage and favorable communication--performance tradeoffs in the studied settings.

Machine Learning (cs.LG); Signal Processing (eess.SP); Probability (math.PR); Applications (stat.AP); Machine Learning (stat.ML)

arXiv:2602.18396 [cs.LG]

(or arXiv:2602.18396v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.18396 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TSP.2026.3737023 Focus to learn more DOI(s) linking to related resources

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