FPPFL: FedAVG-based Privacy-Preserving Federated Learning
Abstract
Edge computing can move storage and computing tasks from cloud data centers to the edge of networks, reducing latency. However, this carries security and privacy risks. Federated learning can protect privacy by transferring training models from edge compute nodes to local devices, which then train models on local datasets. But the parameters transmitted between local devices and edge nodes may contain raw data, which can be stolen. To address this, this paper proposes a FedAVG-based privacy-preserving federated learning (FPPFL) scheme to provide low latency and privacy protection. It optimizes key generation parameters with an improved Paillier homomorphic encryption algorithm, allowing for low-complexity exponential operations in the encryption stage, shortening data transmission time and protecting model parameters, while maintaining accuracy. Experiments demonstrate the improved Paillier algorithm achieves the same security level as benchmark schemes and outperforms them in computational complexity.
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