PS-DFL: A Parallel Sequential Decentralized Federated Learning Framework to Facilitate DGA Interpretation Related to Transformer Fault Diagnosis

Recently, data-driven methods have demonstrated excellent performance in diagnosing faults in power transformers. However, concerns about data privacy and computational resources arise due to the requirement of collecting and training large numbers of labeled samples in a centralized manner. Although centralized federated learning (CFL) provides optimal solutions, the dependence on central servers engenders issues associated with reliability and increased communication expenditures. Meanwhile, existing decentralized federated learning (DFL) frameworks are not specifically designed for industrial applications with unbalanced data and high communication costs. To tackle these issues, this paper proposes a novel parallel sequential DFL (PS-DFL) framework that exchanges information directly between client pairs, thus overcoming the limitation caused by the central server in CFL. In particular, by combining parallel training with sequential communication techniques, the impact of unbalanced data on model performance is mitigated, as well as communication costs are reduced. The experiment conducted on IEC TC 10 and other public datasets demonstrate that, despite handling tasks involving unbalanced data, the diagnostic accuracy is maintained at 97.09%. Furthermore, the communication overhead is significantly lower than that of existing methods, thereby proving its feasibility for industrial deployment within the power grid.