Diffusion models have shown great potential for vision-related tasks,
particularly for image generation. However, their training is typically
conducted in a centralized manner, relying on data collected from publicly
available sources. This approach may not be feasible or practical in many
domains, such as the medical field, which involves privacy concerns over data
collection. Despite the challenges associated with privacy-sensitive data, such
domains could still benefit from valuable vision services provided by diffusion
models. Federated learning (FL) plays a crucial role in enabling decentralized
model training without compromising data privacy. Instead of collecting data,
an FL system gathers model parameters, effectively safeguarding the private
data of different parties involved. This makes FL systems vital for managing
decentralized learning tasks, especially in scenarios where privacy-sensitive
data is distributed across a network of clients. Nonetheless, FL presents its
own set of challenges due to its distributed nature and privacy-preserving
properties. Therefore, in this study, we explore the FL strategy to train
diffusion models, paving the way for the development of federated diffusion
models. We conduct experiments on various FL scenarios, and our findings
demonstrate that federated diffusion models have great potential to deliver
vision services to privacy-sensitive domains.

通过实验和研究，我们发现联邦扩散模型在隐私敏感领域提供视觉服务方面具有巨大潜力。

隐私敏感视觉任务的扩散模型联邦学习

Federated Learning with Diffusion Models for Privacy-Sensitive Vision  Tasks

This article provides a comprehensive synthesis of the recent developments in
synthetic data generation via deep generative models, focusing on tabular
datasets. We specifically outline the importance of synthetic data generation
in the context of privacy-sensitive data. Additionally, we highlight the
advantages of using deep generative models over other methods and provide a
detailed explanation of the underlying concepts, including unsupervised
learning, neural networks, and generative models. The paper covers the
challenges and considerations involved in using deep generative models for
tabular datasets, such as data normalization, privacy concerns, and model
evaluation. This review provides a valuable resource for researchers and
practitioners interested in synthetic data generation and its applications.

该文章综合介绍了最近在通过深度生成模型生成合成数据方面的研究进展，重点关注表格数据集。我们特别概述了在隐私敏感数据背景下合成数据生成的重要性。此外，我们强调了使用深度生成模型相对于其他方法的优势，并详细解释了底层概念，包括无监督学习、神经网络和生成模型。该论文涵盖了在对表格数据集使用深度生成模型时涉及的挑战和考虑因素，如数据归一化、隐私问题和模型评估。本综述为对合成数据生成及其应用感兴趣的研究人员和实践者提供了有价值的资源。

深度生成模型、合成表格数据与差分隐私：概述与综述

Deep Generative Models, Synthetic Tabular Data, and Differential  Privacy: An Overview and Synthesis

Privacy-sensitive data is stored in autonomous vehicles, smart devices, or
sensor nodes that can move around with making opportunistic contact with each
other. Federation among such nodes was mainly discussed in the context of
federated learning with a centralized mechanism in many works. However, because
of multi-vendor issues, those nodes do not want to rely on a specific server
operated by a third party for this purpose. In this paper, we propose a
wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative
machine learning organized by the nodes physically nearby. WAFL can develop
generalized models from Non-IID datasets stored in distributed nodes locally by
exchanging and aggregating them with each other over opportunistic node-to-node
contacts. In our benchmark-based evaluation with various opportunistic
networks, WAFL has achieved higher accuracy of 94.8-96.3% than the
self-training case of 84.7%. All our evaluation results show that WAFL can
train and converge the model parameters from highly-partitioned Non-IID
datasets over opportunistic networks without any centralized mechanisms.

本文提出了一种分布式的合作式机器学习方法，即 WAFL，它能够通过无线自组网络在非 IID 分散节点之间交换和聚合数据，从而构建广义模型，并在多种机会式网络中实现高达 94.8-96.3% 的准确率。

无线自组临时联盟学习：一种完全分布式的合作式机器学习

Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning

We propose algorithms to train production-quality n-gram language models
using federated learning. Federated learning is a distributed computation
platform that can be used to train global models for portable devices such as
smart phones. Federated learning is especially relevant for applications
handling privacy-sensitive data, such as virtual keyboards, because training is
performed without the users' data ever leaving their devices. While the
principles of federated learning are fairly generic, its methodology assumes
that the underlying models are neural networks. However, virtual keyboards are
typically powered by n-gram language models for latency reasons.
We propose to train a recurrent neural network language model using the
decentralized FederatedAveraging algorithm and to approximate this federated
model server-side with an n-gram model that can be deployed to devices for fast
inference. Our technical contributions include ways of handling large
vocabularies, algorithms to correct capitalization errors in user data, and
efficient finite state transducer algorithms to convert word language models to
word-piece language models and vice versa. The n-gram language models trained
with federated learning are compared to n-grams trained with traditional
server-based algorithms using A/B tests on tens of millions of users of virtual
keyboard. Results are presented for two languages, American English and
Brazilian Portuguese. This work demonstrates that high-quality n-gram language
models can be trained directly on client mobile devices without sensitive
training data ever leaving the devices.

提出使用联邦学习算法来训练生产级的 n-gram 语言模型，以解决在处理隐私敏感数据时面临的数据安全问题，该算法特别适用于便携式设备，如智能手机。同时通过将递归神经网络语言模型近似为能快速部署在设备上进行推理的 n-gram 模型，实现了联邦学习在虚拟键盘等场景下的应用。