Latency Analysis of Consortium Blockchained Federated Learning
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Abstract (arXiv)
A decentralized federated learning architecture is proposed to apply to the Businesses-to-Businesses scenarios by introducing the consortium blockchain in this paper. We introduce a model verification mechanism to ensure the quality of local models trained by participators. To analyze the latency of the system, a latency model is constructed by considering the work flow of the architecture. Finally the experiment results show that our latency model does well in quantifying the actual delays.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 10 May 2021.
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References · 6
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
- 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
- 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
- 2018Blockchained On-Device Federated Learningcited by 105
- 2019Privacy-Preserving Blockchain-Based Federated Learning for IoT Devicescited by 12
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