Using Decentralized Aggregation for Federated Learning with Differential
  Privacy
Hadeel Abd El-Kareem Abd El-Moaty Saleh Ana Fernández-Vilas Manuel Fernández-Veiga asser El-Sonbaty
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.