While supervised federated learning approaches have enjoyed significant
success, the domain of unsupervised federated learning remains relatively
underexplored. In this paper, we introduce a novel federated gradient EM
algorithm designed for the unsupervised learning of mixture models with
heterogeneous mixture proportions across tasks. We begin with a comprehensive
finite-sample theory that holds for general mixture models, then apply this
general theory on Gaussian Mixture Models (GMMs) and Mixture of Regressions
(MoRs) to characterize the explicit estimation error of model parameters and
mixture proportions. Our proposed federated gradient EM algorithm demonstrates
several key advantages: adaptability to unknown task similarity, resilience
against adversarial attacks on a small fraction of data sources, protection of
local data privacy, and computational and communication efficiency.

我们引入了一种新颖的联邦梯度 EM 算法，用于异构混合模型的无监督学习，该算法具有适应未知任务相似性、对数据源的少量敌对攻击具有弹性、保护本地数据隐私和计算与通信效率的关键优势。

无监督联邦学习：具有对抗攻击鲁棒性的异构混合模型的联邦梯度 EM 算法

Unsupervised Federated Learning: A Federated Gradient EM Algorithm for  Heterogeneous Mixture Models with Robustness against Adversarial Attacks

Supervised federated learning (FL) enables multiple clients to share the
trained model without sharing their labeled data. However, potential clients
might even be reluctant to label their own data, which could limit the
applicability of FL in practice. In this paper, we show the possibility of
unsupervised FL whose model is still a classifier for predicting class labels,
if the class-prior probabilities are shifted while the class-conditional
distributions are shared among the unlabeled data owned by the clients. We
propose federation of unsupervised learning (FedUL), where the unlabeled data
are transformed into surrogate labeled data for each of the clients, a modified
model is trained by supervised FL, and the wanted model is recovered from the
modified model. FedUL is a very general solution to unsupervised FL: it is
compatible with many supervised FL methods, and the recovery of the wanted
model can be theoretically guaranteed as if the data have been labeled.
Experiments on benchmark and real-world datasets demonstrate the effectiveness
of FedUL. Code is available at this https URL

本文介绍未监督联邦学习，通过共享客户拥有的未标记数据的条件分布，移动类先验概率，并将客户拥有的未标记数据转换为每个客户的替代标记数据，从而在联邦学习中实现模型的共享，即联邦未监督学习（FedUL）模型。FedUL 是通用的未监督联邦学习解决方案，可兼容许多监督 FL 方法，实验结果表明了 FedUL 的有效性。