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A Novel Optimized Asynchronous Federated Learning Framework

arXiv 2111.09487 ↗First posted 18 Nov 2021cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated Learning (FL) since proposed has been applied in many fields, such as credit assessment, medical, etc. Because of the difference in the network or computing resource, the clients may not update their gradients at the same time that may take a lot of time to wait or idle. That's why Asynchronous Federated Learning (AFL) method is needed. The main bottleneck in AFL is communication. How to find a balance between the model performance and the communication cost is a challenge in AFL. This paper proposed a novel AFL framework VAFL. And we verified the performance of the algorithm through sufficient experiments. The experiments show that VAFL can reduce the communication times about 51.02% with 48.23% average communication compression rate and allow the model to be converged faster. The code is available at urlhttps://github.com/RobAI-Lab/VAFL

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 18 Nov 2021.

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References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2021Model-Contrastive Federated Learningcited by 762
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  9. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  10. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  11. 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
  12. 2018A generic framework for privacy preserving deep learningcited by 103
7 more
  1. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  2. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  3. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 77
  4. 2020Experiments of Federated Learning for COVID-19 Chest X-ray Imagescited by 31
  5. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  6. 2020Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systemscited by 9
  7. 2020LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learningcited by 4

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