Exame de Qualificação: Secure Aggregation Protocols for Federated Learning

Orientadores:
Fábio Borges de Oliveira - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Renato Portugal - Laboratório Nacional de Computação Científica - LNCC (presidente)
Bruno Richard Schulze - Laboratório Nacional de Computação Científica - LNCC
Lisandro Zambenedetti Granville - Universidade Federal do Rio Grande do Sul - UFRGS

Suplentes:
Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC

Resumo:

FEDERATED LEARNING (FL) IS A MACHINE LEARNING MODEL WHERE SEVERAL DEVICES WORK TOGETHER TO TRAIN STATISTICAL MODELS UNDER THE COORDINATION OF A CENTRAL SERVER THAT AGGREGATES THE LOCAL MODELS OF EACH DEVICE INTO A GLOBAL MODEL. FL ENSURES THAT A GLOBAL MODEL IS TRAINED BY KEEPING THE DATA ON EACH DEVICE. HOWEVER, EVEN IF ONLY THE GRADIENTS OF THE LOCAL MODELS ARE SHARED WITH THE CENTRAL SERVER, SOME ATTACKS CAN RECONSTRUCT THE USER DATA, GIVING A MALICIOUS SERVER THE POSSIBILITY OF VIOLATING THE PRINCIPLE OF FL, WHICH IS TO GUARANTEE THE PRIVACY OF LOCAL DATA. THUS, IT IS NECESSARY TO ENSURE THAT THE CENTRAL SERVER CAN ONLY BE AWARE OF THE RESULT OF THE AGGREGATION OF ALL LOCAL MODELS. IN THIS WORK, WE PROPOSE A PRELIMINARY VERSION OF AN AGGREGATION PROTOCOL BASED ON HOMOMORPHIC ENCRYPTION, DINING CRYPTOGRAPHERS NETWORKS (DC-NETS), AND SHAMIR'S SECRET SHARING. WITH THESE THREE TECHNIQUES, WE CAN ENSURE PRIVACY, NON-COLLUDING, AND FAULT TOLERANCE TO THE AGGREGATION PROTOCOL. TO PROVE THE CONCEPT OF THE PROPOSED PROTOCOL, WE PERFORMED TWO SIMULATIONS OF FL IN MNIST HANDWRITTEN DIGIT-RECOGNITION TASK AND SHOWED ITS FEASIBILITY