Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
This repository contains the official code for the paper ""Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks"".
"Accepted to FL NeurIPS workshop 2022".
We provide scripts to run the algorithms, which are put under scripts/. Here is an example to run the script:
cd scripts
bash fedavg.sh
To run SC-NIID, modify partition to sc_niid_dir, and sc_niid2 for Non-IID label dir and Non-IID Label Skew respectively. To run C-NIID, modify partition to sc_old_niid_dir, and sc_old_niid2 for Non-IID label dir and Non-IID Label Skew respectively.
The descriptions of parameters are as follows:
| Parameter | Description |
|---|---|
| ntrials | The number of total runs. |
| rounds | The number of communication rounds per run. |
| num_users | The number of clients. |
| frac | The sampling rate of clients for each round. |
| local_ep | The number of local training epochs. |
| local_bs | Local batch size. |
| lr | The learning rate for local models. |
| momentum | The momentum for the optimizer. |
| model | Network architecture. Options: TODO |
| dataset | The dataset for training and testing. Options are discussed above. |
| partition | How datasets are partitioned. Options: homo, noniid-labeldir, noniid-#label1 (or 2, 3, ..., which means the fixed number of labels each party owns). |
| datadir | The path of datasets. |
| logdir | The path to store logs. |
| log_filename | The folder name for multiple runs. E.g., with ntrials=3 and log_filename=$trial, the logs of 3 runs will be located in 3 folders named 1, 2, and 3. |
| alg | Federated learning algorithm. Options are discussed above. |
| beta | The concentration parameter of the Dirichlet distribution for heterogeneous partition. |
| local_view | If true puts local test set for each client |
| gpu | The IDs of GPU to use. E.g., TODO |
| print_freq | The frequency to print training logs. E.g., with print_freq=10, training logs are displayed every 10 communication rounds. |
We have also added the Mix-4 codes and scripts for each algorithm under the mix4/ foloder. You can simply run the script for each algorithm to generate the results.
We have included a jupyter notebook file here which shows how angle measure works on CIFAR-10 dataset.
If you had any questions, please feel free to contact me at [email protected]