Large-scale high-quality data sourced from different providers have been proven to effectively enhance the application efficiency of Artificial Intelligence (AI). However, for data security and privacy protection considerations, the shared modeling of multi- source data requires more efficient and safer privacy computing solutions. To this end, WeBank, which has been focusing on exploring and promoting federated learning, uses the leading FATE (Federated AI Technology Enabler) open source platform to help users quickly build federated learning solutions. Homomorphic encryption (HE) is a commonly used privacy technology in federated learning. HE enables computation directly on the encrypted data, without use of a secret key. HE thereby ensures data security, though at a significant c

Join for free to read