FedSAP: Secure Federated Learning in SDN-IoT via DRL-Enabled Social Attribute Perception

Federated learning (FL) is an innovative distributed privacy-preserving machine learning paradigm, which enables participants to collaboratively train artificial intelligence (AI) models without disclosing private data. Nevertheless, malicious participants have the potential to introduce vicious models via poisoning attacks, which jeopardizes the convergence and accuracy of the global model in FL. In this article, we propose a secure FL distributed architecture based on deep deterministic policy gradient (DDPG), which advances the accuracy of the global model and enhances system robustness. Specifically, we model the accuracy optimization problem with the goal of minimizing the overall loss function of participating devices during each FL iteration. Furthermore, we design the device nodes selection mechanism, named FedSAP, which leverages social attribute perception. Particularly, we first construct the device node selection problem as a Markov decision process (MDP), and then apply social attribute perception and attribute information to the state space ensuring the reliability of the device. Moreover, the long short term memory (LSTM) algorithm is introduced into the actor-critic network structure to learn part of the hidden state through memory inference. The extensive experimental results show that FedSAP can effectively select reliable nodes and significantly improve the accuracy of the global model.