TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

• URL: http://arxiv.org/abs/2211.00735v1
• Date: Tue, 1 Nov 2022 20:31:55 GMT
• Title: TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments
• Authors: Vivek Khimani and Shahin Jabbari
• Abstract summary: We introduce TorchFL, a performant library for bootstrapping federated learning experiments. TorchFL is built on a bottom-up design using PyTorch and Lightning. Being built on a bottom-up design using PyTorch and Lightning, TorchFL provides ready-to-use abstractions for models, datasets, and FL algorithms.
• Score: 4.075095403704456
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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