Federated learning involves a central processor that works with multiple agents to find a global model. The process consists of repeatedly exchanging estimates, which results in the diffusion of information pertaining to the local private data. Such a scheme can be inconvenient when dealing with sensitive data, and therefore, there is a need for the privatization of the algorithms. Furthermore, the current architecture of a server connected to multiple clients is highly sensitive to communication failures and computational overloads at the server. Thus in this work, we develop a private multi-server federated learning scheme, which we call graph federated learning. We use cryptographic and differential privacy concepts to privatize the federated learning algorithm that we extend to the graph structure. We study the effect of privatization on the performance of the learning algorithm for general private schemes that can be modeled as additive noise. We show under convexity and Lipschitz conditions, that the privatized process matches the performance of the non-private algorithm, even when we increase the noise variance.
翻译:联邦学习涉及一个中央处理器,它与多个代理商一起工作,寻找一个全球模型。这一过程包括反复交换估计数,这导致传播与当地私人数据有关的信息。这种计划在处理敏感数据时可能会不方便,因此,需要将算法私有化。此外,目前与多个客户连接的服务器结构对于通信故障和服务器计算超负荷非常敏感。因此,我们在这个工作中开发了一个私人多服务器联合学习计划,我们称之为图形联合学习。我们使用加密和不同隐私概念将我们扩展到图形结构的联邦学习算法私有化。我们研究了私有化对一般私人计划学习算法的绩效的影响,这些私人计划可以建模为添加噪音。我们用混凝土和Lipschitz条件显示,私有化过程与非私人算法的绩效相匹配,即使我们增加了噪音差异。