Distributed Training Techniques for Intelligent Model in Space-Based Information Networks

In addressing the issues of data distribution heterogeneity, outdated models, and data privacy and security in distributed training of intelligent models, a federated learning architecture of intelligent models was designed based on blockchain technology and applied to space-based information networks. A secure and efficient training method for intelligent models was proposed based on this architecture, where a differential privacy noise mechanism, the blockchain technology and a parameter evaluation method were introduced to effectively deal with privacy leakage, poisoning attacks and single-point failure threats. Meanwhile, using a model aggregation method based on the minimized delay, the model training was accelerated via the processes of intra-orbit and inter-orbit model broadcasting and block broadcasting. The simulation results indicated that the proposed method enables intelligent models of different structures to converge rapidly, shorten the model training time, and effectively deal with security and privacy threats.