Federated Concept-Based Models: Interpretable models with distributed supervision
Università della Svizzera italiana（瑞士联邦理工学院）；University of Liechtenstein（利亨斯坦大学）；Politecnico di Torino（都灵理工大学）；IBM Research Zurich（IBM 苏黎世研究实验室）
Dario Fenoglio, Arianna Casanova, Francesco De Santis, Gabriele Dominici, Johannes Schneider, Pietro Barbiero, Giovanni De Felice, Marc Langheinrich, Martin Gjoreski
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely available at scale within a single data source. Federated Learning (FL) could alleviate this limitation by enabling cross-institutional training over concept annotations distributed across multiple data owners. Yet, FL lacks interpretable modeling paradigms. Integrating CMs with FL is non-trivial: although FL supports heterogeneous and non-stationary client participation, it typically assumes a fixed shared architecture, whereas CMs may require architectural adaptation as the available concept set evolves. We propose Federated Concept-based Models (F-CMs), a new methodology for deploying CMs in evolving FL settings. F-CMs aggregate concept-level information across institutions and efficiently adapt the model architecture to changes in concept supervision while preserving privacy. Empirically, F-CMs maintain accuracy and intervention effectiveness comparable to training settings with full concept supervision, while outperforming on average non-adaptive federated baselines. Notably, F-CMs enable interpretable inference on concepts unavailable to a given institution, a key novelty over existing approaches.
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed in different organizations, i.e., the curse of isolated data islands. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework FedGL, which is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global s…
Department of Biomedical Informatics and Data Science, School of Medicine at Yale University（耶鲁大学医学院生物医学信息学与数据科学系）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Software, Shandong University（山东大学软件学院）；Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University（山东大学与南洋理工大学联合人工智能研究中心）
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data. Analyzing distributed data is essential in many applications such as medical, financial, and manufacturing data analyses due to privacy, and confidentiality concerns. In addition, interpretability of the obtained model has an important role for practical applications of the federated learning systems. By centralizing intermediate representations, which are individually constructed in each party, the prop…
Shanghai Jiao Tong University（上海交通大学）；Institute for Network Sciences and Cyberspace, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学网络科学与网络空间研究院，北京信息科学与技术国家研究中心）
Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality and quantity of cyberattack data. Such NN-based approaches are also hard to interpret for improving efficiency and scalability. In this paper, we design a new local-global computation paradigm, FEDFOREST, a novel learning-based NIDS by combining the interpretable Gradient Boosting Decision Tree (GBDT) and Federated Learning (FL) framework. Specifically, FEDFOREST is composed of …
University of Chinese Academy of Sciences（中国科学院大学）
This paper investigates distantly supervised relation extraction in federated settings. Previous studies focus on distant supervision under the assumption of centralized training, which requires collecting texts from different platforms and storing them on one machine. However, centralized training is challenged by two issues, namely, data barriers and privacy protection, which make it almost impossible or cost-prohibitive to centralize data from multiple platforms. Therefore, it is worthy to investigate distant supervision in the federated learning paradigm, which decouples the model training…
TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning
Shanxi Key Laboratory of Industrial Internet Security, College of Computer Science and Technology（工业互联网安全山西省重点实验室，计算机科学与技术学院）；Taiyuan University of Technology（太原科技大学）
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（清华大学深圳国际研究生院，清华大学，深圳，中国）；Shenzhen Research Institute of Big Data, Shenzhen, China（深圳大数据研究院，深圳，中国）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China（香港大学电子与电气工程系，香港，中国）；Yinwang Intelligent Technology Co. Ltd., Shenzhen, China（云网智能科技有限公司，深圳，中国）
Recently, the ever-growing demand for privacy-oriented machine learning has motivated researchers to develop federated and decentralized learning techniques, allowing individual clients to train models collaboratively without disclosing their private datasets. However, widespread adoption has been limited in domains relying on high levels of user trust, where assessment of data compatibility is essential. In this work, we define and address low interoperability induced by underlying client data inconsistencies in federated learning for tabular data. The proposed method, iFedAvg, builds on fede…
With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with …
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；Department of Computer Science, City University of Hong Kong（城市大学计算机科学系）；Shenzhen International Center For Industrial And Applied Mathematics, Shenzhen Research Institute of Big Data（深圳国际工业与应用数学中心、深圳大数据研究院）
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the detection model, a series of techniques have been developed. However, existing work still leaves much to be desired, such as privacy disclosure and communication cost. To address these issues, we propose FedSup, a client-edge-cloud framework for privacy and efficient fatigue detection. Inspired by the federated learning technique, FedSup intelligently utilizes the collaboration…
Viterbi School of Engineering（维特比工程学院）；University of Southern California（南加利福尼亚大学）
Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data privacy. An essential but rarely studied challenge in FL is label deficiency at the edge. This problem is even more pronounced in FL compared to centralized training due to the fact that FL users are often reluctant to label their private data. Furthermore, due to the heterogeneous nature of the data at edge devices, it is crucial to develop personalized models. In this paper we propose self-supervised federated learni…
Department of Medical Data Science（医学数据科学系）；Wakayama Medical University（福冈医疗大学）；Department of Biomedical Sciences and Informatics（生物医学科学与信息学系）；Doshisha University（多摩大学）；Department of Biostatistics（生物统计学系）
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
Cooperative Innovation Center, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所协同创新中心）；University of Chinese Academy of Sciences（中国科学院大学）；JD Technology Group（京东科技集团）；University of Technology Sydney（悉尼科技大学）
University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）
Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this …
IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Center for Computational Health（计算健康中心）
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled through our framework. We then develop an inference approach that allows us to synthesize a more expressive global network without additional supervision, data pooling and with as few as a single communication round. We then demonstrate the efficacy of our approach on federated lea…
We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features. We address the lack-of-feature issue by extending the feature space through vertical federated learning with a feature-rich party and tackle the sample-scarce issue by performing adversarial domain adaptation from the sample-rich source party to the target party. In this work, we focus on financial applications where…
Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the cloud in order to train and improve such systems. Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy. However, since ML models are most commonly trained with label supervision, we need a way to extract labels on ed…
Label Granularity Skew in Federated Learning with Hierarchical Image Classification
School of Computer Science and Engineering, Soongsil University（崇实大学计算机科学与工程学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
FedNAMs: Performing Interpretability Analysis in Federated Learning Context
Department of Electrical and Computer Engineering University of California San Diego（电气与计算机工程系加州大学圣地亚哥分校）；Department of Computer Science and Engineering University of California San Diego（计算机科学与工程系加州大学圣地亚哥分校）
A Universal Metric of Dataset Similarity for Cross-silo Federated Learning
Department of Biomedical Informatics, Columbia University, NY, USA（生物医学信息学系，哥伦比亚大学）；New York Genome Center, NY, USA（纽约基因中心）；Department of Computer Science, Columbia University, NY, USA（计算机科学系，哥伦比亚大学）
FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications（北京邮电大学国家灾备工程技术研究中心）；JIUTIAN Research, China Mobile（中国移动九天研究院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；University of Leicester（莱斯特大学）
University of Padova（帕多瓦大学）；Politecnico di Torino（都灵理工大学）
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealistically assume labeled data in the remote clients. Here we propose a novel task (FFREEDA) in which the clients' data is unlabeled and the server accesses a source labeled dataset for pre-training only. To solve FFREEDA, we propose LADD, which leverages the knowledge of the pre-trained model by employing self-supervision with ad-hoc regularization techniq…
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；Harbin Engineering University（哈尔滨工程大学）
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges. Transfer learning and multi-task learning techniques have been leveraged to mitigate the supervision sparsity and collaborate multiple heterogeneous domains correspondingly. Recent years, the sensitive nature of users' private data raises another challenge for sentiment classification, i.e., data privacy protection. In this paper, we resort to federated learning for multiple domain sentiment classification under …
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2.0 model pre-trained by self supervision. Carried out on the well-known TED-LIUM 3 dataset, our experiments show that such a model can obtain, with no use of a language model, a word error rate of 10.92% on the official TED-LIUM 3 test set, without sharing any data from the different users. We also analyse the ASR performance for speakers depending to their participation to the federated learning. Since federated learning was first introduced for privacy purposes, we also measure its ability …
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
National Engineering Laboratory for Big Data Analytics (NEL-BDA)（大数据分析国家工程实验室）；School of Mathematics and Statistics（数学与统计学学院）；Ministry of Education Key Lab for Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）
Politecnico di Torino（都灵理工大学）；University of Tübingen（蒂宾根大学）；Sapienza University of Rome（罗马第一大学）；Politecnico di Milano（米兰理工大学）；Italian Institute of Technology（意大利理工学院）
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates of the parameters computed locally by each client. This raises a problem, known as statistical heterogeneity, because the clients may have different data distributions (i.e. domains). This is only partly alleviated by clustering the clients. Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the da…
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院清华-伯克利深圳学院）；Tsinghua University（清华大学）
In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations. To fully exploit the underlying patterns of these distributed data for accurate power prediction, federated learning is needed as a collaborative but privacy-preserving training scheme. However, current federated learning frameworks are polarized towards addressing either the horizontal or vertical separation of data, and tend to overlook the case where both are present. Further…
Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance concerns related to data curation. However, several interesting real-world graph data characteristics viz. label deficiency and downstream task heterogeneity are not taken into consideration in current FedGRL setups. In this paper, we consider a realistic and novel problem setting, wherein cross-silo clients have access to vast amounts of unlabeled data with limited or no labeled data and additionally have diverse down…
