Introduction

This is the implementation of our paper: FedL2G: Learning to Guide Local Training in Heterogeneous Federated Learning.

Datasets and Environments

Due to the file size limitation, we only upload the statistics (config.json) of the Cifar10 dataset in the Dirichlet setting. All the code for the baselines, datasets, and environments is publicly available in the popular repository HtFLlib.

System

• main.py: System configurations.
• total.sh: Command lines to run experiments for FedL2G with default hyperparameter settings on Linux.
• flcore/:
  • clients/: The code on clients for both FedL2G-l and FedL2G-f.
  • servers/: The code on servers for both FedL2G-l and FedL2G-f.
  • trainmodel/: The code for some heterogeneous client models.
• utils/:
  • data_utils.py: The code to read the dataset.
  • mem_utils.py: The code to record memory usage.
  • result_utils.py: The code to save results to files.

Training and Evaluation

All codes are stored in ./system. Just run the following commands.

cd ./system
sh run_me.sh