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Journal Article
Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks
- Authors
- Qazi Waqas Khan, Chan‐Won Park, Rashid Ahmad, Atif Rizwan, Anam Nawaz Khan, Sunhwan Lim, Do Hyeun Kim
- Issue Date
- 2024-06
- Citation
- IET Collaborative Intelligent Manufacturing, v.6, no.3, pp.1-23
- ISSN
- 2516-8398
- Publisher
- IET
- Language
- English
- Type
- Journal Article
- Abstract
- Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.
- KSP Keywords
- Communication efficiency, Convergence performance, Digital Twin, Distributed Computation, Energy Forecasting, Energy Prediction, Energy data, Enhance performance, Federated learning, Individual privacy, Large network
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
