AIoT-MLSys-Lab/FedAIoT AIoT-MLSys-Lab/FedAIoT

  • Stars
    star
    44
  • Rank 631,117 (Top 13 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 1 year ago
  • Updated 3 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
AIoT-MLSys-Lab/FedAIoT
github

AIoT-MLSys-Lab

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things

More Repositories

1

Efficient-LLMs-Survey

[TMLR 2024] Efficient Large Language Models: A Survey
960
star
2

SVD-LLM

Official Code for "SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression"
Python
84
star
3

DeepAA

[ICLR 2022] "Deep AutoAugment" by Yu Zheng, Zhi Zhang, Shen Yan, Mi Zhang
Python
60
star
4

FedRolex

[NeurIPS 2022] "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" by Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang
Python
54
star
5

arch2vec

[NeurIPS 2020] "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?" by Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang
Python
49
star
6

CATE

[ICML 2021 Oral] "CATE: Computation-aware Neural Architecture Encoding with Transformers" by Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang
Python
18
star
7

AIoT-Survey

Artificial Intelligence of Things: A Survey
5
star
8

Distream

[SenSys 2020] "Distream: Scaling Live Video Analytics with Workload-Adaptive Distributed Edge Intelligence" by Xiao Zeng, Biyi Fang, Haichen Shen, Mi Zhang
Go
4
star
9

AIoT-MLSys-Lab.github.io

Homepage of OSU AIoT and MLSys Lab
JavaScript
4
star
10

MSUNet

[NeurIPS 2019 Google MicroNet Challenge] MSUNet is an efficient model that won the 4th place in the Google MicroNet Challenge CIFAR-100 Track hosted at NeurIPS 2019 designed by Yu Zheng, Shen Yan, Mi Zhang
Python
4
star
11

Mercury

[SenSys 2021] "Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling" by Xiao Zeng, Ming Yan, Mi Zhang
Python
1
star