论文标题
部分可观测时空混沌系统的无模型预测
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
论文作者
论文摘要
在私人联合学习（FL）中，服务器从大量客户端汇总了私人更新，以训练机器学习模型。在这种情况下，主要的挑战是将隐私与学习模型的分类精度以及客户和服务器之间传达的位数之间的分类精度保持平衡。先前的工作通过设计隐私感知的压缩机制（称为最小方差无偏（MVU）机制）实现了良好的权衡，该机制在数值上解决了一个优化问题来确定机制的参数。本文通过在数值设计过程中引入新的插值过程来建立在此基础上，该过程允许进行更有效的隐私分析。结果是新的插值MVU机制，它更可扩展，具有更好的隐私性权衡权衡，并在各种数据集上提供了有关沟通效率的私人FL的结果。
In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated between the clients and server. Prior work has achieved a good trade-off by designing a privacy-aware compression mechanism, called the minimum variance unbiased (MVU) mechanism, that numerically solves an optimization problem to determine the parameters of the mechanism. This paper builds upon it by introducing a new interpolation procedure in the numerical design process that allows for a far more efficient privacy analysis. The result is the new Interpolated MVU mechanism that is more scalable, has a better privacy-utility trade-off, and provides SOTA results on communication-efficient private FL on a variety of datasets.
