DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. (arXiv:2207.07779v1 [cs.CR])

Federated learning has emerged as a privacy-preserving machine learning
approach where multiple parties can train a single model without sharing their
raw training data. Federated learning typically requires the utilization of
multi-party computation techniques to provide strong privacy guarantees by
ensuring that an untrusted or curious aggregator cannot obtain isolated replies
from parties involved in the training process, thereby preventing potential
inference attacks. Until recently, it was thought that some of these secure
aggregation techniques were sufficient to fully protect …

decentralized federated learning privacy trust