Decentralized Model Training with Homomorphic Encryption
This image depicts a decentralized model training utilizing homomorphic encryption to ensure privacy during AI training. Here's how the process works:
Data owners, each with their unique secret keys, encrypt their local gradients, which are partial computations derived from their own data. These encrypted gradients are then sent to an aggregator. The role of the aggregator is crucial—they sum up all the encrypted gradients from the data owners. It's important to note that this aggregation happens while the gradients are still encrypted, preserving the privacy of individual data contributions.
Once the aggregator has combined all the encrypted gradients, they send the aggregated encrypted gradient, along with a public key, to the trainer. The trainer uses this public key to decrypt the aggregated gradient and update the model's parameters. This decryption step does not reveal individual data or gradients but only the combined information, ensuring that the trainer cannot reverse-engineer or access the original data.
Homomorphic encryption is the underlying magic that makes this process secure. It's a form of encryption that allows computations to be carried out on ciphertexts, generating an encrypted result which, when decrypted, matches the result of operations performed on the plaintexts. This means that data can be encrypted and processed without ever being exposed in its raw form, ensuring that personal or sensitive information remains confidential throughout the training process.
This method strikes a balance between leveraging valuable data for AI development and upholding stringent privacy standards, thus facilitating a collaborative AI training environment where data owners can contribute to model improvement without compromising data security.
Last updated