We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties. We do so by distributing the role of the aggregator in FL into multiple servers and having them run secure multiparty computation (MPC) protocols to perform model and feature aggregation and apply differential privacy (DP) to the final released model. While a naive solution would have the clients delegating the entirety of training to run in MPC between the servers, our optimized solution, which supports purely global and also global-local models updates with privacy-preserving, drastically reduces the amount of computation and communication performed using multiparty computation. The experimental results also show the effectiveness of our protocols.

翻译：我们提出了一种新颖的端到端隐私保护框架，针对联邦学习中标签未在所有参与方间共享的纵向切分场景（特征在不同客户端间切分），通过三种适用于不同部署场景的高效协议实例化该框架，同时保障输入隐私与输出隐私。具体而言，我们将联邦学习中的聚合器角色分散到多个服务器，由这些服务器运行安全多方计算协议以执行模型与特征的聚合操作，并对最终发布的模型施加差分隐私保护。与客户端将完整训练过程委托给服务器间安全多方计算的朴素方案不同，我们提出的优化方案支持纯全局模型更新及全局-局部混合模型的隐私保护更新，大幅减少了安全多方计算所需的数据计算量及通信开销。实验结果也验证了我们协议的有效性。