Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent studies have illustrated that FL still suffers from information leakage as adversaries try to recover the training data by analyzing shared parameters from local clients. To deal with this issue, differential privacy (DP) is adopted to add noise to the gradients of local models before aggregation. It, however, results in the poor performance of gradient-based interpretability methods, since some weights capturing the salient region in feature map will be perturbed. To overcome this problem, we propose a simple yet effective adaptive differential privacy (ADP) mechanism that selectively adds noisy perturbations to the gradients of client models in FL. We also theoretically analyze the impact of gradient perturbation on the model interpretability. Finally, extensive experiments on both IID and Non-IID data demonstrate that the proposed ADP can achieve a good trade-off between privacy and interpretability in FL.
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）；Peng Cheng Laboratory（鹏城实验室）；WeBank（微众银行）；The Hong Kong Polytechnic University（香港理工大学）
Aalto University（阿尔托大学）；University of Waterloo（滑铁卢大学）
Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, research at the intersection of security/privacy and ML has flourished. Typically such work has focused on individual types of security/privacy concerns and mitigations thereof. However, in real-life deployments, an ML model will need to be protected against several concerns simultaneously. A protection mechanism optimal for one security or privacy concern may interact negatively with mechanisms intended to address other …
Towards Case-based Interpretability for Medical Federated Learning
The Netherlands Cancer Institute（荷兰癌症研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；Utrecht University（乌得勒支大学）；INESC TEC
Exploring the Interpretability of Forecasting Models for Energy Balancing Market
Department of Informatics, University of Oslo（信息学院，奥斯陆大学）；Department of Electric Energy, Norwegian University of Science and Technology（电力能源系，挪威科学与技术大学）
The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which yields high-quality, interpretable results. Previously, we have proposed an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition. In this work we set out to explore the trade-off between …
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
University of Texas at Dallas Richardson, TX 75082（德克萨斯大学达拉斯分校）；Vanderbilt University Medical Center Nashville, TN 37232（范德比尔特大学医学中心）；Virginia Tech Blacksburg, VA 24061（弗吉尼亚理工大学）
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This paper addresses the problem of generating interpretable clusters, given features of interest that signify interpretability to an end-user, by optimizing interpretability in addition to common clustering objectives. We propose a $β$-interpretable clustering algorithm that ensures that…
Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach
Center for Intelligent Decision Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an, China（智能决策与机器学习中心，管理学院，西安交通大学）；Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong（数学系，香港 Baptist 大学）
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization
Centre for Research on Intelligent Software Engineering（智能软件工程研究中心）；School of Computing and Information Systems（计算与信息系统学院）；Singapore Management University（新加坡管理大学）
Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data. However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization. In such an attack, a client's contribution during training and information about their data set is revealed through analyzi…
Laboratory for Data Security -- EPFL（数据安全实验室 -- 洛桑联邦理工学院）
Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works…
Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring
African Institute for Mathematical Sciences (AIMS)（非洲数学科学研究所）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所）；Ludwig Maximilian University of Munich（慕尼黑路易斯·马克西米利安大学）；University of Ferrara（费拉拉大学）
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
Centre for Frontier AI Research (CFAR)（前沿人工智能研究所在）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Agency for Science, Technology and Research（科技研究局）；D-ITET；ETH Zurich（苏黎世联邦理工学院）；CCDS, Nanyang Technological University（南洋理工大学CCDS）
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
INESC TEC（INESC TEC（葡萄牙系统与计算机工程研究所））；University of Porto（波尔图大学）；Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所IGD）；TU Darmstadt（达姆施塔特工业大学）
Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Xi’an Jiaotong University（西安交通大学）；University of Oklahoma（俄克拉荷马大学）；University of Washington（华盛顿大学）
Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have been extensively used in a wide variety of industries such as healthcare and criminal justice. However, the fairness issues in these models have long been criticized, and the use of big data and machine learning algorithms in the construction of scoring systems heightens this concern. In this paper, we propose a general framework to create fairness-aware, data-driven scoring systems. First, we develop a social welfa…
PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
Osnabrück University, Joint Lab Artificial Intelligence & Data Science（奥斯纳布鲁克大学，人工智能与数据科学联合实验室）；Osnabrück University, Institute of Mathematics（奥斯纳布鲁克大学，数学学院）；Osnabrück University, Semantic Information Systems Group（奥斯纳布鲁克大学，语义信息系统组）；German Research Center for Artificial Intelligence (DFKI), Research Department Plan-Based Robot Control（德国人工智能研究中心（DFKI），计划型机器人控制研究部）；Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Sensors and Modelling（莱比锡农业工程与生物经济研究所（ATB），传感器与建模部门）
