Nowadays, the ubiquitous usage of mobile devices and networks have raised concerns about the loss of control over personal data and research advance towards the trade-off between privacy and utility in scenarios that combine exchange communications, big databases and distributed and collaborative (P2P) Machine Learning techniques. On the other hand, although Federated Learning (FL) provides some level of privacy by retaining the data at the local node, which executes a local training to enrich a global model, this scenario is still susceptible to privacy breaches as membership inference attacks. To provide a stronger level of privacy, this research deploys an experimental environment for FL with Differential Privacy (DP) using benchmark datasets. The obtained results show that the election of parameters and techniques of DP is central in the aforementioned trade-off between privacy and utility by means of a classification example.

翻译：如今，移动设备和网络的普及引发了对个人数据失控的担忧，尤其在结合通信交换、大型数据库与分布式协作（P2P）机器学习技术的场景中，隐私与效用之间的权衡成为研究热点。另一方面，尽管联邦学习（FL）通过将数据保留在本地节点（该节点执行本地训练以丰富全局模型）提供了一定程度的隐私保护，但其仍易遭受成员推理攻击等隐私泄露风险。为提供更强的隐私保护水平，本研究利用基准数据集部署了集成差分隐私（DP）的联邦学习实验环境。结果表明，在分类示例中，差分隐私的参数选择与技术手段对于上述隐私与效用的权衡具有决定性作用。