Flower Full-Compliant Implementation of Federated Learning with Homomorphic Encryption

Flower Full-Compliant Implementation of Federated Learning with Homomorphic Encryption

연구 분야: Cryptography

학회: 2024 IEEE Symposium on Computers and Communications (ISCC)

Federated Learning exploits local model training to aggregate and create a global model without sharing raw data. Each client trains a local model and shares it to aggregate a global one. Several works demonstrate that starting from trained weights, it’s possible to reconstruct the original used data. For this reason, the research and industrial world introduced the Homomorphic Encryption technique to encrypt the transmitted local model’s weights. This approach protects the trained weights from a hypothetical malicious aggregator server that can not perform operations over plaintext weights. In the proposed work, we implement a Federated Learning solution applying Homomorphic Encryption using the Flower framework and the Tenseal library. Our solution follows best practices for custom aggregation strategies with the Flower framework, making it possible to provide it to the community.