We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy. Our techniques are provably instance-optimal for mean estimation, meaning that they can adapt to the ``hardness of the problem" with minimal interactivity. We demonstrate the effectiveness of our approach on real-world datasets by achieving favorable compression rates without the need for tuning.

翻译：我们提出了一种新技术，用于在私有联邦学习中减少通信量，同时无需设置或调整压缩率。我们的在线方法能够根据训练过程中引入的误差自动调整压缩率，并通过使用安全聚合和差分隐私来保持可证明的隐私保证。我们的技术在均值估计上具有可证明的实例最优性，意味着它们能以最少的交互适应问题的“难度”。我们在真实数据集上展示了该方法的有效性，无需调整即可实现优越的压缩率。