  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 against inference
attacks coming from a curious aggregator. However, recent research has
demonstrated that a curious aggregator can successfully launch a disaggregation
attack to learn information about model updates of a target party. This paper
presents DeTrust-FL, an efficient privacy-preserving federated learning
framework for addressing the lack of transparency that enables isolation
attacks, such as disaggregation attacks, during secure aggregation by assuring
that parties' model updates are included in the aggregated model in a private
and secure manner. DeTrust-FL proposes a decentralized trust consensus
mechanism and incorporates a recently proposed decentralized functional
encryption (FE) scheme in which all parties agree on a participation matrix
before collaboratively generating decryption key fragments, thereby gaining
control and trust over the secure aggregation process in a decentralized
setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms
state-of-the-art FE-based secure multi-party aggregation solutions in terms of
training time and reduces the volume of data transferred. In contrast to
existing approaches, this is achieved without creating any trust dependency on
external trusted entities.
