A Federated Learning Scheme for Enhanced Privacy Protection and Resistance to Byzantine Attacks
Abstract
As distributed data sources grow, and privacy protection becomes more important, federated learning (FL) is becoming an important way to model by collaboration under data privacy constraints. However, existing federated learning schemes still suffer from data privacy preservation and security problems. We propose a federated learning scheme that also helps better preserve privacy and defend against Byzantine attacks. This type of scheme is based on discrete stochastic noise, using multi-layer privacy preservation to protect private data and defend against Byzantine attacks. First, we propose a dual-layer protection mechanism of adding discrete noise and differential privacy noise, both of which aim at protecting against inference attacks and data privacy. Second, we introduce a dual server architecture to separate aggregation and privacy processing to mitigate the server collusion risk. Lastly, we use a secure aggregation defense strategy to mitigate the negative effect of Byzantine nodes on the global model. It is also shown that our improved scheme performs better than the original LSFL scheme in terms of its inference resistance and privacy protection.
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