Ieee transactions on neural networks and learning systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems. Communication often overlooking training eficiency and data heterogeneity. In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data.
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Decentralized federated learning (dfl) has gained popularity due to its robustness and elimination of centralized coordination requirements.
In this paradigm, clients actively.
Ieee transactions on neural networks and learning systems（简称ieee tnnls）是ieee计算智能学会核心刊物，神经网络和学习系统方面的国际顶级杂志。
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