Enhancing the Performance of Model Pruning in Over-the-Air Federated Learning with Non-IID Data
Author:
Affiliation:
1. School of Electrical Engineering & Computer Science (SEECS), National University of Sciences & Technology (NUST),Islamabad,Pakistan
2. Schulich School of Engineering, University of Calgary,Calgary,Alberta,Canada
Funder
Natural Sciences and Engineering Research Council of Canada (NSERC)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10615255/10615259/10615547.pdf?arnumber=10615547
Reference14 articles.
1. 6g mobile-edge empowered metaverse: Requirements, technologies, challenges and research directions;Yu;arXiv preprint,2022
2. Communication-efficient learning of deep networks from decentralized data;McMahan,2017
3. A Survey on Over-the-Air Computation
4. Structured Pruning for Deep Convolutional Neural Networks: A Survey
5. Deep Compression for Efficient and Accelerated Over-the-Air Federated Learning
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning;IEEE Internet of Things Magazine;2024-09
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