# PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models

arXiv: https://arxiv.org/abs/2409.18245 · HTML: https://mlsanity.com/paper/2409.18245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 14 of 14)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models](https://mlsanity.com/paper/2306.04675/) (2023)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [Do We Train on Test Data? Purging CIFAR of Near-Duplicates](https://mlsanity.com/paper/1902.00423/) (2019)
- [A Non-Parametric Test to Detect Data-Copying in Generative Models](https://mlsanity.com/paper/2004.05675/) (2020)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model](https://mlsanity.com/paper/2305.04063/) (2023)
- [BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning](https://mlsanity.com/paper/2007.03856/) (2020)
- [Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks](https://mlsanity.com/paper/2311.16538/) (2023)
- [ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain](https://mlsanity.com/paper/1904.12059/) (2019)
- [DECORAIT -- DECentralized Opt-in/out Registry for AI Training](https://mlsanity.com/paper/2309.14400/) (2023)
- [Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach](https://mlsanity.com/paper/2107.10243/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Framework for Cryptographic Verifiability of End-to-End AI Pipelines](https://mlsanity.com/paper/2503.22573/) (2025)

## Abstract (arXiv)

We present PDFed, a decentralized, aggregator-free, and asynchronous federated learning protocol for training image diffusion models using a public blockchain. In general, diffusion models are prone to memorization of training data, raising privacy and ethical concerns (e.g., regurgitation of private training data in generated images). Federated learning (FL) offers a partial solution via collaborative model training across distributed nodes that safeguard local data privacy. PDFed proposes a novel sample-based score that measures the novelty and quality of generated samples, incorporating these into a blockchain-based federated learning protocol that we show reduces private data memorization in the collaboratively trained model. In addition, PDFed enables asynchronous collaboration among participants with varying hardware capabilities, facilitating broader participation. The protocol records the provenance of AI models, improving transparency and auditability, while also considering automated incentive and reward mechanisms for participants. PDFed aims to empower artists and creators by protecting the privacy of creative works and enabling decentralized, peer-to-peer collaboration. The protocol positively impacts the creative economy by opening up novel revenue streams and fostering innovative ways for artists to benefit from their contributions to the AI space.