A federated learning flight operation data sharing algorithm for balancing privacy and utility

The need for data security and privacy protection makes it difficult to directly share some flight operation data. Federated learning (FL) achieves data availability that is invisible, but when faced with honest but curious opponents, it still faces the threat of reverse attacks and the risk of privacy leakage. The inadequate performance of FL in striking a balance between privacy protection and model utility is addressed by the suggested artificial bee colony and dual Rényi differential privacy federated learning (ABC-2RDP-FL) flight operation data privacy protection algorithm, which is based on dual Rényi differential privacy (RDP) and artificial bee colony (ABC). In ABC-2RDP-FL, a dual RDP protection mechanism is designed to measure privacy budgets more strictly and improve privacy protection performance. After that, an ABC-based FL hyperparameter optimization approach is suggested to enhance model performance while striking a compromise between model utility and privacy protection. Finally, the effectiveness of the proposed method was validated using public data and flight operation data.