There are some naming differences which are an artifact of our iterative development. We will first outline how the names in the codebase translate to the names in the paper:

1. FedAvgBatch = FedAvg: the hyperparameter beta corresponds to eta
2. Naive = Local Training: the hyperparameter beta corresponds to alpha
3. FTFA = PerAvgEpochBatch: the hyperparameter beta, learning_rate corresponds to eta, alpha respectively
4. RTFA = PerRidgeEpochBatch: the hyperparameter beta, learning_rate, user_ridge_penalty corresponds to eta, alpha, lambda respectively
5. MAML-FL-HF = PerAvgHF: the hyperparameter beta, learning_rate, delta_HF corresponds to eta, alpha, delta respectively
6. MAML-FL-FO = PerAvg: the hyperparameter beta, learning_rate corresponds to eta, alpha respectively

The PerAvgHFOneStep, PerAvgOneStep, and PerAvgEpochBatchOneStep algorithms correspond to the PerAvgHF, PerAvg, and PerAvgEpochBatch algorithms with one batch of personalization.

Example workflow for {Dataset Name}:

0. Have conda installed; run the following command to install the packages necessary for running the code conda env create -f environment.yaml inside the fed-learn-cuda11 folder. run conda activate fed-learn-cuda11 to activate the environment.
1. Download data from by navigating to ./preprocess_and_pretrain/FedML/data/{Dataset Name}/ Then run sh download_{Dataset Name}.sh
2. Create dataset for preprocessing by running python {Dataset Name}_make_preprocessing_data.py in the ./preprocess_and_pretrain/ folder
3. Run preprocessing by running python {Dataset Name}_pretrain_main.py in the ./preprocess_and_pretrain/ folder
4. Create preprocessed datasets to be fed into last layer net federated learning algorithms by running python {Dataset Name}_make_preprocessed_datasets.py in the ./preprocess_and_pretrain/ folder
5. In the fed-learn-code folder, run the following with [...] replaced with the desired hyperparameter

python main.py --algorithm=[...] --dataset=[...] --model=[...] --beta=[...] --learning_rate=[...] --user_ridge_penalty=[...] --delta_HF=[...] --datasetnumber=[...] --seed=[...] ---no-cuda=[...] --decimate=50 --batch_size=32 --local_epochs=20  --num_global_iters=401 --numusers=20 --optimizer=SGD --rank=0 --times=1 --personal_epochs=10

The algorithm options are ["pFedMe", "PerAvg", "FedAvgBatch", "PerAvgEpochBatch", "PerAvgHF", "Naive", "PerRidgeEpochBatch", "PerAvgOneStep", "PerAvgEpochBatchOneStep", "PerAvgHFOneStep"]

The dataset options are ["prep_FederatedEMNIST", "prep_Femnist", "prep_Shakespeare", "prep_CIFAR100", "prep_Stackoverflownwp"]

The model options are ["cnn", "rnn", "resnet", "rnnstackoverflow"].

no-cuda specifies whether to use gpu for training. All of our testing was on gpu.

See fed-learn-code/main.py for details on each option. Checkout the P2-sweeps folder to see what choices of hyperparameters we searched over. Checkout P3-seed-sweeps and P4-dataset-sweeps to find the best parameters.