Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work, we address these challenges within the trusted aggregator model by introducing a novel approach called the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM), achieving both objectives simultaneously. In particular, CEPAM leverages the rejection-sampled universal quantizer (RSUQ), a construction of randomized vector quantizer whose resulting distortion is equivalent to a prescribed noise, such as Gaussian or Laplace noise, enabling joint differential privacy and compression. Our CEPAM provides the additional benefit of privacy adaptability, allowing clients and the server to customize privacy protection based on required accuracy and protection. We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM through experimental evaluations. Moreover, we assess CEPAM's utility performance using MNIST dataset, demonstrating that CEPAM surpasses baseline models in terms of learning accuracy.

翻译：在去中心化私有数据上通过联邦学习（FL）训练机器学习模型面临两个关键挑战：通信效率与隐私保护。本研究在可信聚合器模型框架下，通过提出一种名为通信高效且隐私可适应机制（CEPAM）的新方法，同时实现这两个目标。具体而言，CEPAM利用拒绝采样通用量化器（RSUQ）——一种随机向量量化器结构，其产生的失真等效于预设噪声（如高斯或拉普拉斯噪声），从而能够联合实现差分隐私与压缩。我们的CEPAM还具备隐私可适应性的额外优势，允许客户端与服务器根据所需的精度和保护级别定制隐私保护方案。我们从理论上分析了CEPAM的隐私保障，并通过实验评估探讨了用户隐私与CEPAM精度之间的权衡关系。此外，我们使用MNIST数据集评估了CEPAM的效用性能，结果表明CEPAM在学习精度方面优于基线模型。