Training diffusion models with federated learning

A communication-efficient model for cross-silo federated image generation

Bachelor thesis (2023)

Authors

M.B.J. de Goede Electrical Engineering, Mathematics and Computer Science

Contributors

B.A. Cox Data-Intensive Systems - EEMCS (mentor)
Jérémie Decouchant Data-Intensive Systems - EEMCS (mentor)
Q. Wang Embedded Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Matthijs de Goede
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Published Date

30-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Probabilistic Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training, compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.