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.Comment: 20 pages, 15 figures, 4 table