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This paper addresses performance degradation in federated learning (FL) due to data heterogeneity and variable participation frequencies among nodes. They introduce PMFL, a model-contrastive FL framework that incorporates historical training information to improve model consistency and reduce performance fluctuations. PMFL demonstrates superior performance compared to existing FL methods in heterogeneous scenarios through extensive experimentation.
Overcome performance degradation in heterogeneous federated learning by contrasting current models with historical training states, leading to more stable and accurate global models.
Federated Learning (FL) enables multiple nodes to collaboratively train a model without sharing raw data. However, FL systems are usually deployed in heterogeneous scenarios, where nodes differ in both data distributions and participation frequencies, which undermines the FL performance. To tackle the above issue, this paper proposes PMFL, a performance-enhanced model-contrastive federated learning framework using historical training information. Specifically, on the node side, we design a novel model-contrastive term into the node optimization objective by incorporating historical local models to capture stable contrastive points, thereby improving the consistency of model updates in heterogeneous data distributions. On the server side, we utilize the cumulative participation count of each node to adaptively adjust its aggregation weight, thereby correcting the bias in the global objective caused by different node participation frequencies. Furthermore, the updated global model incorporates historical global models to reduce its fluctuations in performance between adjacent rounds. Extensive experiments demonstrate that PMFL achieves superior performance compared with existing FL methods in heterogeneous scenarios.
