Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures,including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations.

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This makes it difficult to create physically faithful drift test cases or to provide specifications of data models that should be avoided when deploying a machine learning model. In this study, we demonstrate how these shortcomings can be overcome by pairing machine learning robustness validation with physical optics. We examine the role raw sensor data and differentiable data models can play in controlling performance risks related to image dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases. The experiments presented here show that the average decrease in model performance is ten to four times less severe than under post-hoc augmentation testing. Second, the gradient connection between task and data models allows for drift forensics that can be used to specify performance-sensitive data models which should be avoided during deployment of a machine learning model. Third, drift adjustment opens up the possibility for processing adjustments in the face of drift. This can lead to speed up and stabilization of classifier training at a margin of up to 20% in validation accuracy. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.

可解释的人工智能（XAI）的新兴领域旨在为当今强大但不透明的深度学习模型带来透明度。尽管本地XAI方法以归因图的形式解释了个体预测，从而确定了重要特征的发生位置（但没有提供有关其代表的信息），但全局解释技术可视化模型通常学会的编码的概念。因此，两种方法仅提供部分见解，并留下将模型推理解释的负担。只有少数当代技术旨在将本地和全球XAI背后的原则结合起来，以获取更多信息的解释。但是，这些方法通常仅限于特定的模型体系结构，或对培训制度或数据和标签可用性施加其他要求，这实际上使事后应用程序成为任意预训练的模型。在这项工作中，我们介绍了概念相关性传播方法（CRP）方法，该方法结合了XAI的本地和全球观点，因此允许回答“何处”和“ where”和“什么”问题，而没有其他约束。我们进一步介绍了相关性最大化的原则，以根据模型对模型的有用性找到代表性的示例。因此，我们提高了对激活最大化及其局限性的共同实践的依赖。我们证明了我们方法在各种环境中的能力，展示了概念相关性传播和相关性最大化导致了更加可解释的解释，并通过概念图表，概念组成分析和概念集合和概念子区和概念子区和概念子集和定量研究对模型的表示和推理提供了深刻的见解。它们在细粒度决策中的作用。

在非IID本地数据集的情况下，即客户数据的分布是异质的，联合学习会受到损失。应对这一挑战的一种有希望的方法是最近提出的方法Fedaux，即甚至高度异构客户数据的联合蒸馏增强，并具有强劲的结果。 Fedaux是一种部分$（\ epsilon，\ delta）$ - 差异化的私有方法，就客户的私人数据仅在其参与的部分培训中受到保护。这项工作贡献了完全差异化的私人修改，称为Fedauxfdp。我们进一步对正则化多项式逻辑回归的$ L_2 $ - 敏感性做出了贡献。在大规模图像数据集的深网实验中，具有强大差异隐私的FedauxFDP在仅在一次通信回合中，在非IID客户端数据上的其他同样私有化的SOTA基线表现出色。修改方法的完全私有化导致所有数据异质性的准确性降低可忽略不计。

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

像人类一样自然而然地处理和保留新信息的能力是在训练神经网络时受到极大追捧的壮举。不幸的是，传统优化算法通常需要在培训时间和更新WRT期间可用的大量数据。培训过程完成后，新数据很难。实际上，当出现新数据或任务时，由于神经网络容易遭受灾难性遗忘，因此可能会丢失先前的进展。灾难性遗忘描述了当神经网络在获得新信息时完全忘记以前的知识时，这种现象。我们提出了一种新颖的培训算法，称为培训，通过解释我们利用层面相关性传播的方式，以保留神经网络在培训新数据时已经在先前任务中学习的信息。该方法在一系列基准数据集以及更复杂的数据上进行评估。我们的方法不仅成功地保留了神经网络中旧任务的知识，而且比其他最先进的解决方案更有效地进行了资源。

除了机器学习（ML）模型的令人印象深刻的预测力外，最近还出现了解释方法，使得能够解释诸如深神经网络的复杂非线性学习模型。获得更好的理解尤其重要。对于安全 - 关键的ML应用或医学诊断等。虽然这种可解释的AI（XAI）技术对分类器达到了重大普及，但到目前为止对XAI的重点进行了很少的关注（Xair）。在这篇综述中，我们澄清了XAI对回归和分类任务的基本概念差异，为Xair建立了新的理论见解和分析，为Xair提供了真正的实际回归问题的示范，最后讨论了该领域仍然存在的挑战。

许多领域的研究表明，转移学习（TL）非常适合提高具有少量样品的数据集中深度学习（DL）模型的性能。这种经验成功引发了对具有功能神经影像数据的认知解码分析的应用的兴趣。这里，我们系统地评估了从全脑功能磁共振成像（FMRI）数据的认知状态（例如，观看面部或房屋图像）的解码的TL。我们首先在大型公共FMRI数据集中预先列出两个DL架构，随后在独立实验任务和完全独立的数据集中评估其性能。预先训练的模型始终如一地达到更高的解码精度，并且通常需要较少的训练时间和数据，而不是模型变形，这些模型变体没有预先接受培训，明确强调预制培训的好处。我们证明，这些益处是由于预先训练的模型在使用新数据培训时重用了许多学习功能的这些益处，从而深入了解导致预训练的好处的机制。然而，在解释预先训练模型的解码决策时，我们还通过DL模型对全脑认知解码进行了差别挑战，因为这些已经学会了在不可预见的情况下利用FMRI数据和识别单个认知状态的违反直觉方式。

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.

Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularity, it has been observed that Federated Learning yields suboptimal results if the local clients' data distributions diverge. To address this issue, we present Clustered Federated Learning (CFL), a novel Federated Multi-Task Learning (FMTL) framework, which exploits geometric properties of the FL loss surface, to group the client population into clusters with jointly trainable data distributions. In contrast to existing FMTL approaches, CFL does not require any modifications to the FL communication protocol to be made, is applicable to general non-convex objectives (in particular deep neural networks) and comes with strong mathematical guarantees on the clustering quality. CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models. We verify our theoretical analysis in experiments with deep convolutional and recurrent neural networks on commonly used Federated Learning datasets.