Federated learning has emerged as a promising approach for collaborative and
privacy-preserving learning. Participants in a federated learning process
cooperatively train a model by exchanging model parameters instead of the
actual training data, which they might want to keep private. However, parameter
interaction and the resulting model still might disclose information about the
training data used. To address these privacy concerns, several approaches have
been proposed based on differential privacy and secure multiparty computation
(SMC), among others. They often result in large communication overhead and slow
training time. In this paper, we propose HybridAlpha, an approach for
privacy-preserving federated learning employing an SMC protocol based on
functional encryption. This protocol is simple, efficient and resilient to
participants dropping out. We evaluate our approach regarding the training time
and data volume exchanged using a federated learning process to train a CNN on
the MNIST data set. Evaluation against existing crypto-based SMC solutions
shows that HybridAlpha can reduce the training time by 68% and data transfer
volume by 92% on average while providing the same model performance and privacy
guarantees as the existing solutions.Comment: 12 pages, AISec 201