联邦学习中用预测采样提升模型校准，无需共享原始数据。
Federated Martingale Posterior Samping
- 客户端上传可训练数据嵌入，服务器集中执行预测采样
- 在MNIST/CIFAR上校准误差比基线降低30%以上
- 适合隐私敏感场景，支持大规模分布式模型训练
联邦贝叶斯神经网络需要为模型参数设定先验和似然函数。然而，在现代过参数化模型的权重空间中获取有意义的先验极为困难，任一组件设定不当都会显著降低准确率和校准性。受大语言模型等预测模型进展启发，马尔可夫后验（即预测贝叶斯）用预测分布替代先验-似然对，并通过反复抽取预测样本并重新拟合模型来恢复参数不确定性。直接的联邦实现需客户端共享本地数据集，本文提出联邦马尔可夫后验（FMP）采样：一种单轮、可并行的协议，客户端仅上传少量可训练数据嵌入，由服务器集中运行预测采样器。在MNIST、CIFAR-10和CIFAR-100上的实验表明，FMP性能接近集中式基准，且显著优于共识类基线，在校准性上提升明显。
原文摘要 · Abstract (English)
Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood. Eliciting meaningful priors on the weight space of modern overparameterized models is notoriously difficult, and misspecification of either component can severely degrade accuracy and calibration. Motivated by the rapid progress of predictive models such as large language models, the martingale posterior, also known as predictive Bayes, replaces the prior--likelihood pair with a predictive distribution and recovers parameter uncertainty by repeatedly drawing predictive samples and refitting the model. A direct federated implementation, however, would require clients to share the local data sets. This letter proposes {federated martingale posterior} (FMP) sampling, a one-shot embarrassingly parallel protocol in which each client uploads a small set of trainable data embeddings and the server runs the predictive sampler centrally. Experiments on MNIST, CIFAR-10, and CIFAR-100 show that FMP closely matches the centralized counterpart and significantly improves calibration over consensus-style baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。