<ul><li>Researchers introduce Traversal Learning (TL), a novel approach to address the decreased quality in distributed learning paradigms such as Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL). TL adopts a unique strategy where the model traverses nodes during forward propagation and performs backward propagation on the orchestrator, effectively implementing centralized learning (CL) principles within a distributed environment. TL outperformed other DL methods and improved accuracy across various datasets representing different domains. TL represents a significant advancement in DL methodologies that preserves data privacy while maintaining performance.</li></ul>

Traversal Learning Coordination For Lossless And Efficient Distributed Learning

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