Projet de recherche doctoral numero :8388


Date depot: 6 octobre 2022
Titre: Cybersecure Federated Learning
Directrice de thèse: Maria POTOP-BUTUCARU (LIP6)
Directeur de thèse: Sébastien TIXEUIL (LIP6)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Systèmes et réseaux

Resumé: The ambition of the research project is to enhance the concept of FML (Federated Machine Learning) and MADRL (Multi-agent deep reinforcement learning) to a new concept of CF-DRL (CyberSecure Distributed Reinforced Learning). Follow the FML principles, CF-DRL will utilize local data for model training and aggregated iteratively at the cloud. However, since FML is associated with frequent information exchange and communication overheads, the thesis will propose and evaluate the effectiveness of different optimization methods. Moreover, to achieve real-time and low overhead offloading decisions, we will experiment with a novel two-timescale DRL approach, consisting of a fast-timescale and a slow-timescale training process, minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation and service caching placement. Moreover, we will leverage secure computation techniques and aggregation primitives to privately and securely combine local ML training, in order to update a global model.

Doctorant.e: Legheraba Mohamed