Decentralized Federated Learning: A Survey on Security and Privacy

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it …

advantages architecture cs.ai cs.cr cs.lg data decentralized features federated learning network performance privacy security security and privacy stat.ml survey the exchange updates