Kilian Pfeiffer, Martin Rapp, Ramin Khalili, Jörg Henkel
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
in Transactions on Machine Learning Research (TMLR) 06/2023
https://openreview.net/pdf?id=XJIg4kQbkv

• Install requirements with python3 -m pip install -r requirements.txt
• Datasets CIFAR-10, CIFAR-100, CINIC-10 require no manual downloads
• For IMDB, parts are already stored in the repository, others are automatically downloaded
• Files for SHAKESPEARE are already in the repository's data folder
• For the FEMNIST experiments, it is required to download the inputs and targets from here or here and put them into the data/femnist folder
• Data for XCHEST is automatically loaded from here

Each run (with specific arguments) creates an folder (run_HASH) with a specific hash of the configuration. If a specific run is run again, the results are overridden. The experiment folder is created at runs/{session_tag}/. Every 25 FL rounds, plots are generated. Alternativly, plots can be generated manually using python3 utils/plots.py --session_tag {SESSION_TAG}

Main experiments from Table 2 in the paper can be run by using

• python3 main.py --algorithm CoCoFL --network MobileNet --dataset CIFAR10 --data_distribution NONIID --seed 10 --noniid_dirichlet 0.1 --n_rounds 1000 --lr 0.1 --lr_schedule 600 800 --torch_device cuda

FedAvg and Centralized Baselines can be run by using

• --algorithm FedAvg (full resources)
• --algorithm FedAvgDropDevices Devices that do not have full resources are dropped from training
• --algorithm Centralized

For profiling with ARM a pip wheel is required that is compiled with the qnnpack quantization backend. For x64, the fbgemm backend is used. python3 profiling.py --network MobileNet --architecture X64 This creates json files in theprofiling folder. The tables used for the experiments are already put in nets/QuanizedNets/{NN-structure}/tables/