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Back to https://github.com/Kwangkee/FL

University of Michigan, https://github.com/symbioticlab
Mosharaf Chowdhury, https://www.mosharaf.com/
Fan Lai, http://www-personal.umich.edu/~fanlai/
https://jaewonchung.me/about.html

fedscale.ai

A scalable and extensible federated learning engine and benchmark, http://fedscale.ai/
Open-Source Systems for Federated Learning | Stanford MLSys #48, https://www.youtube.com/watch?v=TcbOMbg4F9g

FedScale

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale, https://arxiv.org/abs/2105.11367
[slides] http://www-personal.umich.edu/~fanlai/assets/docs/fedscale-icml-slides.pdf from http://www-personal.umich.edu/~fanlai/

image

Second, existing benchmarks often overlook system speed, connectivity, and availability of the clients (e.g., FedML (He et al., 2020) and Flower (Beutel et al., 2021)). This discourages FL efforts from considering system efficiency and leads to overly optimistic statistical performance (§2).

FedScale 에 비하면 Flower 는 장난감. Massive number of clients 지원, 다양한 practical FL setting 지원

FedScale, https://github.com/symbioticlab/fedscale

FedScale Repo Structure

Repo Root
|---- fedscale          # FedScale source code
  |---- core            # Core of FedScale service
  |---- utils           # Auxiliaries (e.g, model zoo and FL optimizer)
  |---- deploy          # Deployment backends (e.g., mobile)
  |---- dataloaders     # Data loaders of benchmarking dataset

|---- benchmark         # FedScale datasets and configs
  |---- dataset         # Benchmarking datasets

|---- examples          # Examples of implementing new FL designs
|---- docs              # FedScale tutorials and APIs

Swan

Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs, https://arxiv.org/abs/2206.04687

Swan is built within Termux, a Linux Terminal emulator for Android, and can efficiently train unmodified PyTorch models.

Mobile On-device Training의 대안으로 Swan 제시. TFLite, PyTorch Mobile 쓰지 않고, unmodified PyTorch models 그대로 사용.

Back to Papers
Back to https://github.com/Kwangkee/FL