Healthcare industries frequently handle sensitive and proprietary data, and
due to strict privacy regulations, they are often reluctant to share data
directly. In today's context, Federated Learning (FL) stands out as a crucial
remedy, facilitating the rapid advancement of distributed machine learning
while effectively managing critical concerns regarding data privacy and
governance. The fusion of federated learning and quantum computing represents a
groundbreaking interdisciplinary approach with immense potential to
revolutionize various industries, from healthcare to finance. In this work, we
proposed a federated learning framework based on quantum tensor networks, which
leverages the principles of many-body quantum physics. Currently, there are no
known classical tensor networks implemented in federated settings. Furthermore,
we investigated the effectiveness and feasibility of the proposed framework by
conducting a differential privacy analysis to ensure the security of sensitive
data across healthcare institutions. Experiments on popular medical image
datasets show that the federated quantum tensor network model achieved a mean
receiver-operator characteristic area under the curve (ROC-AUC) between
0.91-0.98. Experimental results demonstrate that the quantum federated global
model, consisting of highly entangled tensor network structures, showed better
generalization and robustness and achieved higher testing accuracy, surpassing
the performance of locally trained clients under unbalanced data distributions
among healthcare institutions.

提出了基于量子张量网络的联邦学习框架，通过多体量子物理原理充分利用，进行了不平衡数据分布下的差分隐私分析，实验结果显示，联邦量子张量网络模型在医疗图像数据集上达到了 0.91-0.98 的平均接收者操作特征曲线下面积（ROC-AUC），在医疗院所之间的不平衡数据分布下，量子联邦全局模型表现出更好的泛化性能和鲁棒性，实现了更高的测试准确率，超过了本地训练客户端的表现。

联邦分层张量网络：面向医疗领域的协作学习量子人工智能框架

Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum  AI-Driven Framework for Healthcare

Hyperparameter optimization, also known as hyperparameter tuning, is a widely
recognized technique for improving model performance. Regrettably, when
training private ML models, many practitioners often overlook the privacy risks
associated with hyperparameter optimization, which could potentially expose
sensitive information about the underlying dataset. Currently, the sole
existing approach to allow privacy-preserving hyperparameter optimization is to
uniformly and randomly select hyperparameters for a number of runs,
subsequently reporting the best-performing hyperparameter. In contrast, in
non-private settings, practitioners commonly utilize "adaptive" hyperparameter
optimization methods such as Gaussian process-based optimization, which select
the next candidate based on information gathered from previous outputs. This
substantial contrast between private and non-private hyperparameter
optimization underscores a critical concern. In our paper, we introduce
DP-HyPO, a pioneering framework for "adaptive" private hyperparameter
optimization, aiming to bridge the gap between private and non-private
hyperparameter optimization. To accomplish this, we provide a comprehensive
differential privacy analysis of our framework. Furthermore, we empirically
demonstrate the effectiveness of DP-HyPO on a diverse set of real-world and
synthetic datasets.

DP-HyPO 是一种桥接私人和非私人超参数优化方法的里程碑式框架，并提供了具有全面差分隐私分析的高效性。

DP-HyPO：自适应私有超参数优化框架

DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework

Institutions in highly regulated domains such as finance and healthcare often
have restrictive rules around data sharing. Federated learning is a distributed
learning framework that enables multi-institutional collaborations on
decentralized data with improved protection for each collaborator's data
privacy. In this paper, we propose a communication-efficient scheme for
decentralized federated learning called ProxyFL, or proxy-based federated
learning. Each participant in ProxyFL maintains two models, a private model,
and a publicly shared proxy model designed to protect the participant's
privacy. Proxy models allow efficient information exchange among participants
using the PushSum method without the need of a centralized server. The proposed
method eliminates a significant limitation of canonical federated learning by
allowing model heterogeneity; each participant can have a private model with
any architecture. Furthermore, our protocol for communication by proxy leads to
stronger privacy guarantees using differential privacy analysis. Experiments on
popular image datasets, and a pan-cancer diagnostic problem using over 30,000
high-quality gigapixel histology whole slide images, show that ProxyFL can
outperform existing alternatives with much less communication overhead and
stronger privacy.

本文提出了 ProxyFL，即基于代理的联邦学习，作为一种通信高效、隐私性强，并且能够支持模型异构的去中心化联邦学习方案。基于代理的模型交换运用 PushSum 方法，能消除传统联邦学习的模型同构限制，并使用差分隐私分析得到更强的隐私保护。实验结果表明，ProxyFL 在通信开销上具有优势，并且隐私性更强。