TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

With the increased legislation around data privacy, federated learning (FL) has emerged as a promising technique that allows the clients (end-user) to collaboratively train deep learning (DL) models without transferring and storing the data in a centralized, third-party server. Despite the theoretical success, FL is yet to be adopted in real-world systems due to the hardware, computing, and various infrastructure constraints presented by the edge and mobile devices of the clients. As a result, simulated datasets, models, and experiments are heavily used by the FL research community to validate their theories and findings. We introduce TorchFL, a performant library for (i) bootstrapping the FL experiments, (ii) executing them using various hardware accelerators, (iii) profiling the performance, and (iv) logging the overall and agent-specific results on the go. Being built on a bottom-up design using PyTorch and Lightning, TorchFL provides ready-to-use abstractions for models, datasets, and FL algorithms, while allowing the developers to customize them as and when required.