Paper Title
Towards Privacy Preservingand Efficiency in Fog Selection for Federated Learning

Federated learning (FL) is an emerging trend related to the concept of distributed Machine Learning (ML).It focuses on a collaborative training process, which is conducted locally on the dataset of the client devices in order to preserve the users’ privacy. Nonetheless, this solution still suffers from many challenges dealing with the privacy, security, and performance. In this research, we aim to enhance privacy, security and performance in federated learning by introducing a novel policy-based FL approach. Our proposed solution ensures reliability, communications security, and heterogeneous privacy (i.e., the users have different privacy attitudes and expectations.). In addition, it guarantees the performance in terms of the dataset quality and scalability. To prove the effectiveness of our model, we perform a security and performance evaluation by assuming a threat model with attackers having different behaviors. The evaluation analysis shows that our proposed model provides a high level of security, a good performance, and a promising solution for the federated learning environments. Keywords - Federated Learning, Collaborative training, Heterogeneous privacy, Policy-based Approach.