AIDRC Seminar Series - Prof. David Naccache

Title:

Federating Under Privacy Constraints: When Federated Learning and Cryptography Meet 

Abstract:

Federated learning consists of aggregating several machine learning tasks into a bigger, global learning endeavor. The aggregation of tasks concerns either the learning phase (federated learning) or the testing phase (federated testing). Typically, federated learning allows several network users to jointly train or interrogate a global model while each user keeps its local dataset private. Federating has several advantages, amongst which the most important are efficiency, i.e., the ability to distribute (parallelize) learning over several machines, and security (because confidential information is not stored or processed at a unique master node). This talk will overview the security challenges posed by federated learning and also investigate how cryptography (notably homomorphic encryption, multiparty computation, and oblivious-transfer-based techniques) may add privacy to federated learning