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Personalized Federated Training of Diffusion Models with Privacy Guarantees
Machine Learning · Submitted 1 Apr 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments show that our framework outperforms non-collaborative training methods, particularly in settings with high data heterogeneity, and effectively reduces biases and imbalances in synthetic data, resulting in fairer downstream models.
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The abstract, sentence by sentence
The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthcare, finance, and biomedical research. Furthermore, access to unrestricted public datasets is increasingly constrained due to rising concerns over privacy, copyright, and competition. Synthetic data has emerged as a promising alternative, and diffusion models -- a cutting-edge generative AI technology -- provide an effective solution for generating high-quality and diverse synthetic data. In this paper, we introduce a novel federated learning framework for training diffusion models on decentralized private datasets. Our framework leverages personalization and the inherent noise in the forward diffusion process to produce high-quality samples while ensuring robust differential privacy guarantees. Our experiments show that our framework outperforms non-collaborative training methods, particularly in settings with high data heterogeneity, and effectively reduces biases and imbalances in synthetic data, resulting in fairer downstream models.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthcare, finance, and biomedical research. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Furthermore, access to unrestricted public datasets is increasingly constrained due to rising concerns over privacy, copyright, and competition. | background 0.99 | 0.04 | 0.02 | asserted 0.99 |
| s2 | Synthetic data has emerged as a promising alternative, and diffusion models -- a cutting-edge generative AI technology -- provide an effective solution for generating high-quality and diverse synthetic data. | background 1.00 | 0.05 | 0.03 | asserted 0.97 |
| s3 | In this paper, we introduce a novel federated learning framework for training diffusion models on decentralized private datasets. | contribution 0.99 | 0.83 | 0.06 | asserted 1.00 |
| s4 | Our framework leverages personalization and the inherent noise in the forward diffusion process to produce high-quality samples while ensuring robust differential privacy guarantees. | method 0.90 | 0.69 | 0.15 | asserted 1.00 |
| s5 | Our experiments show that our framework outperforms non-collaborative training methods, particularly in settings with high data heterogeneity, and effectively reduces biases and imbalances in synthetic data, resulting in fairer downstream models. | result 0.99 | 0.96 | 0.85 | asserted 1.00 |
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The paper
Original abstract
The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthcare, finance, and biomedical research. Furthermore, access to unrestricted public datasets is increasingly constrained due to rising concerns over privacy, copyright, and competition. Synthetic data has emerged as a promising alternative, and diffusion models -- a cutting-edge generative AI technology -- provide an effective solution for generating high-quality and diverse synthetic data. In this paper, we introduce a novel federated learning framework for training diffusion models on decentralized private datasets. Our framework leverages personalization and the inherent noise in the forward diffusion process to produce high-quality samples while ensuring robust differential privacy guarantees. Our experiments show that our framework outperforms non-collaborative training methods, particularly in settings with high data heterogeneity, and effectively reduces biases and imbalances in synthetic data, resulting in fairer downstream models.
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What this paper does with the arXiv papers it cites
What it cites · 11 sentences without a confident label
Cites as support 3 papers
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In practice, if we want to use membership inference attacks to determine the membership of a given image or some of its pixels, two digits $ϵ$ DP guarantee can effectively defend against many strong membership inference attacks [ref] .
Revisiting Membership Inference Under Realistic Assumptions · Main Results · 0.98
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In practice, if we want to use membership inference attacks to determine the membership of a given image or some of its pixels, two digits $ϵ$ DP guarantee can effectively defend against many strong membership inference attacks [ref] .
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? · Main Results · 0.98
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However, this protocol does not inherently guarantee privacy, as diffusion models are prone to memorizing their training data [ref] , and standard privacy-preserving techniques, such as differentially private (DP) training of diffusion models, have yet to be effectively applied, increasing the risk of exposing sensitive information.
White-box Membership Inference Attacks against Diffusion Models · Introduction · 0.96
Cites as background 9 papers
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Current approaches for federated training of diffusion models [ref] focus on training a single global model using only client gradients, which leverages decentralized datasets and avoids the exchange of raw data.
Training Diffusion Models with Federated Learning · Introduction · 0.80
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Current approaches for federated training of diffusion models [ref] focus on training a single global model using only client gradients, which leverages decentralized datasets and avoids the exchange of raw data.
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models · Introduction · 0.80
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Personalization is a widely used strategy in FL to address data heterogeneity and provide client-specific control [ref] .
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity · Related Work · 0.82
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