FedScale References

We are actively developing FedScale, and welcome new implementations from the community. Here are the related papers introducing different components of FedScale:

• FedScale Benchmark: Introduces FedScale datasets and benchmarking runtime.

  @inproceedings{fedscale-icml22,
  title={FedScale: Benchmarking Model and System Performance of Federated Learning at Scale},
  author={Fan Lai and Yinwei Dai and Sanjay S. Singapuram and Jiachen Liu and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2022}
  }
  

• Mobile Backend: Introduces an early mobile prototype that FedScale supports.

  @inproceedings{swan-arxiv,
  title={Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs},
  author={Sanjay Sri Vallabh Singapuram and Fan Lai and Chuheng Hu and Mosharaf Chowdhury},
  booktitle={arXiv arxiv.2206.04687},
  year={2022}
  }
  

• FedScale Early Prototype: Introduces an advanced federated learning client selection mechanism incorporated with FedScale as a third-party toolkit.

  @inproceedings{oort-osdi21,
  title={Oort: Efficient Federated Learning via Guided Participant Selection},
  author={Fan Lai and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
  booktitle={USENIX Symposium on Operating Systems Design and Implementation (OSDI)},
  year={2021}
  }