在现实世界应用中，联合学习（FL）遇到了两个挑战：（1）可伸缩性，尤其是应用于大型物联网网络时； （2）如何使用异质数据对环境进行健全。意识到第一个问题，我们旨在设计一个名为Full-Stack FL（F2L）的新型FL框架。更具体地说，F2L使用层次结构架构，使扩展FL网络可以访问而无需重建整个网络系统。此外，利用层次网络设计的优势，我们在全球服务器上提出了一种新的标签驱动知识蒸馏（LKD）技术来解决第二个问题。与当前的知识蒸馏技术相反，LKD能够训练学生模型，该模型由所有教师模型的良好知识组成。因此，我们提出的算法可以有效地提取区域数据分布（即区域汇总模型）的知识，以减少客户在使用非独立分布数据的FL系统下操作时客户模型之间的差异。广泛的实验结果表明：（i）我们的F2L方法可以显着提高所有全球蒸馏的总体FL效率，并且（ii）F2L随着全球蒸馏阶段的发生而迅速达到收敛性，而不是在每个通信周期中提高。

联合学习（FL）是一个新的人工智能概念，它使得互联网（IoT）设备能够学习协作模型，而无需将原始数据发送到集中的节点进行处理。尽管有许多优势，但在物联网设备上的计算资源较低，交换模型参数的高通信成本使得FL在大型物联网网络中的应用非常有限。在这项工作中，我们为非常大的物联网网络开发了一种新型的FL压缩方案，称为高压联合学习（HCFL）。 HCFL可以减少FL过程的数据负载，而无需更改其结构和超参数。通过这种方式，我们不仅可以显着降低沟通成本，而且使密集学习过程更适应低计算资源的物联网设备。此外，我们研究了IoT设备数量与FL模型的收敛水平之间的关系，从而更好地评估了FL过程的质量。我们在模拟和数学分析中演示了HCFL方案。我们提出的理论研究可以用作最低满意度的水平，证明在满足确定的配置时，FL过程可以实现良好的性能。因此，我们表明HCFL适用于具有许多物联网设备的任何FLENTECTED网络。

本文旨在研究入侵攻击，然后为区块链网络开发新的网络攻击检测框架。具体来说，我们首先在实验室设计和实施区块链网络。该区块链网络将实现两个目的，即为我们的学习模型生成真实的流量数据（包括正常数据和攻击数据），并实施实时实验，以评估我们建议的入侵检测框架的性能。据我们所知，这是第一个在区块链网络中用于网络攻击的实验室中合成的数据集。然后，我们提出了一个新颖的协作学习模型，该模型允许区块链网络中的有效部署来检测攻击。提出的学习模型的主要思想是使区块链节点能够积极收集数据，从其数据中分享知识，然后与网络中的其他区块链节点交换知识。这样，我们不仅可以利用网络中所有节点的知识，而且还不需要收集所有原始数据进行培训，以便在常规的集中学习解决方案等集中式节点上进行培训。这样的框架还可以避免暴露本地数据的隐私以及过多的网络开销/拥堵的风险。密集模拟和实时实验都清楚地表明，我们提出的基于协作的入侵检测框架可以在检测攻击方面达到高达97.7％的准确性。

联邦学习（FL）最近成为网络攻击检测系统的有效方法，尤其是在互联网上（物联网）网络。通过在IOT网关中分配学习过程，FL可以提高学习效率，降低通信开销并增强网络内人检测系统的隐私。在这种系统中实施FL的挑战包括不同物联网中的数据特征的标记数据和不可用的不可用。在本文中，我们提出了一种新的协作学习框架，利用转移学习（TL）来克服这些挑战。特别是，我们开发一种新颖的协作学习方法，使目标网络能够有效地和快速学习来自拥有丰富标记数据的源网络的知识。重要的是，最先进的研究要求网络的参与数据集具有相同的特征，从而限制了入侵检测系统的效率，灵活性以及可扩展性。但是，我们所提出的框架可以通过在各种深度学习模型中交换学习知识来解决这些问题，即使他们的数据集具有不同的功能。关于最近的真实网络安全数据集的广泛实验表明，与基于最先进的深度学习方法相比，拟议的框架可以提高超过40％。

在这项工作中，我们提出了一种新颖的框架来解决联邦学习（FL）的移动应用程序服务的争吵和隐私问题，考虑到移动用户（MUS）/移动应用程序提供者（MAP），隐私的有限计算/通信资源在贡献数据到地图中的MU中的成本，合理性和激励竞争。特别是，该地图首先基于MUS的信息/特征确定FL过程的一组最佳MU。为了缓解隐私意识的讨论问题，每个选定的MU可以加密本地数据的一部分，并除了本地培训过程之外，还可以将加密数据上载到加密培训过程的地图。为此，每个选定的MU可以根据其预期的培训本地数据和隐私保护的加密数据向地图提出合同。为了找到最佳合同，可以最大限度地利用地图和所有参与峰的同时保持整个系统的高学习质量，首先开发一个基于多个实用程序的基于多个实用程序的基于多项基于的一个基于的基于替代的问题。这些实用程序函数占MUS'隐私成本，地图的计算资源有限，地图和MU之间的不对称信息。然后，我们将问题转换为等同的低复杂性问题，并开发轻量级迭代算法，以有效地找到最佳解决方案。具有真实世界数据集的实验表明，我们的框架可以加快培训时间高达49％，提高预测准确性高达4.6倍，同时增强网络的社会福利，即所有参与实体的总实用性，高达114％与基线方法相比，隐私费用考虑。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the $f$-DP approach and introduces a new proof technique which allows us to also analyse group privacy. In particular, for $E$ epochs work and groups of size $g$, we show a $\sqrt{g E}$ DP dependency for batch clipping with shuffling. This is much better than the previously anticipated linear dependency in $g$ and is much better than the previously expected square root dependency on the total number of rounds within $E$ epochs which is generally much more than $\sqrt{E}$.

Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the game-theoretic soundness and convergence bounds for Upper Confidence bounds applied to Trees (UCT), the most popular instantiation of MCTS; however, there remain notable gaps in our understanding of how UCT behaves in practice. In this work, we address one such gap by considering the question of whether UCT can exhibit lookahead pathology -- a paradoxical phenomenon first observed in Minimax search where greater search effort leads to worse decision-making. We introduce a novel family of synthetic games that offer rich modeling possibilities while remaining amenable to mathematical analysis. Our theoretical and experimental results suggest that UCT is indeed susceptible to pathological behavior in a range of games drawn from this family.

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.