Robust Distributed Swarm Learning for Intelligent IoT

In this paper, we study a communication-efficient distributed learning scheme through a holistic integration of federated learning (FL) and particle swarm optimization, called DSL, which is suitable for the implementation of intelligent IoT applications. Since only one selected optimum from all local devices need to report its local model updates to the parameter server, the communication cost of DSL is much reduced compared to its counterpart of standard FL. However, the DSL is vulnerable to adversarial attackers. To achieve Byzantine-resilient DSL, we propose to introduce a shared dataset for scoring local updates to screen attackers. We further provide the convergence analysis to theoretically demonstrate that CB-DSL is superior than the standard FL. Experiment results show that the learning performance of our proposed CB-DSL outperforms the existing benchmarks with only a small amount of globally shared data. It enjoys higher robustness against Byzantine attacks than the vanilla DSL, and has better communication efficiency than the standard FL
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Our code can be found at: https://github.com/fuanxiyin/CB-DSL.git..