Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration

@article{Xu2021HeliosHF,
  title={Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration},
  author={Zirui Xu and Fuxun Yu and Jinjun Xiong and Xiang Chen},
  journal={2021 58th ACM/IEEE Design Automation Conference (DAC)},
  year={2021},
  pages={997-1002}
}
  • Zirui XuFuxun Yu Xiang Chen
  • Published 5 December 2021
  • Computer Science
  • 2021 58th ACM/IEEE Design Automation Conference (DAC)
As Federated Learning (FL) has been widely used for collaborative training, a considerable computational straggler issue emerged: when FL deploys identical neural network models to heterogeneous devices, the ones with weak computational capacities, referred to as stragglers, may significantly delay the synchronous parameter aggregation. Although discarding stragglers from the collaboration can relieve this issue to a certain extent, stragglers may keep unique and critical information learned… 

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