CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption

Cloud-based services for federated learning has received widespread attention for its ability to collaboratively train a model without collecting users' local data. Although there are existing methods such as homomorphic encryption and secure multi-party computation to address the privacy issues associated with the model parameter exchanging during aggregation, these methods will inevitably lead to huge communication overheads or slow down the training time. Functional encryption (FE) is considered as a new approach to address privacy-preserving federated learning probelms, but the only known FE solution has severe security issues such as leaking master private key, and is impractical. Thus, in this paper, we propose CryptoFE, a cloud-based privacy-preserving federated learning aggregation scheme based on FE. Compared with the only existing FE solution, CryptoFE is efficient in aggregation phase, especially when a high model precision is required, and provides formal privacy guarantees for users' gradients. The experiments with real-world data demonstrate the efficeint performance of our proposed scheme.