This work proposes Fed-GLOSS-DP, a novel approach to privacy-preserving learning that uses synthetic data to train federated models. In our approach, the server recovers an approximation of the global loss landscape in a local neighborhood based on synthetic samples received from the clients. In contrast to previous, point-wise, gradient-based, linear approximation (such as FedAvg), our formulation enables a type of global optimization that is particularly beneficial in non-IID federated settings. We also present how it rigorously complements record-level differential privacy. Extensive results show that our novel formulation gives rise to considerable improvements in terms of convergence speed and communication costs. We argue that our new approach to federated learning can provide a potential path toward reconciling privacy and accountability by sending differentially private, synthetic data instead of gradient updates. The source code will be released upon publication.

翻译：摘要：本文提出Fed-GLOSS-DP，一种利用合成数据训练联邦模型的隐私保护学习新方法。在该方法中，服务器基于从客户端接收的合成样本，在局部邻域内恢复全局损失景观的近似值。与先前基于逐点梯度线性近似的方法（如FedAvg）相比，我们的公式能够实现一种全局优化，这在非独立同分布联邦场景中尤为有益。我们还展示了该方法如何严格补充记录级差分隐私。大量结果表明，我们的新公式在收敛速度和通信成本方面带来了显著改进。我们认为，这种联邦学习新方法通过发送差分隐私的合成数据而非梯度更新，可能为协调隐私与问责之间关系提供潜在路径。源代码将在论文发表后公开。