Federated Leaning is an emerging approach to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification or rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the development of accurate classifiers with an effective insight-sharing mechanism that does not break the data integrity. Moreover, we explain how the new architecture can be readily integrated in a blockchain infrastructure to ensure the verifiable and auditable execution of the algorithm. Furthermore, we discuss how this work may set the basis for a more general approach for the design of federated ensemble-learning methods beyond the specific task and architecture discussed in this paper.
Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and help build trust among participants. Verifiable federated learning has become an emerging topic of research that has attracted significant interes…
Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Manchester（曼彻斯特大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlea…
ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
School of Electrical Engineering, Northeast Electric Power University（电气工程学院，东北电力大学）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；State Grid Xiamen Electric Power Supply Company（国家电网厦门供电有限公司）；Department of Electrical Engineering, City University of Hong Kong（电气工程系，香港城市大学）
Trustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized, Verifiable, and Incentive-Aligned Coordination
Marshall School of Business University of Southern California（南加州大学马歇尔商学院）；School of Natural and Computing Sciences University of Aberdeen（阿伯丁大学自然与计算科学学院）
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
School of Computer Engineering, KIIT Deemed to be University（计算机工程学院，KIIT deemed to be 大学）；School of Electronics Engineering, KIIT Deemed to be University（电子工程学院，KIIT deemed to be 大学）
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of federated learning algorithms. Our first contribution is a scalable protocol in which participants exchange correlated Gaussian noise along the edges of a network graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of ou…
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN)（里约格兰德北教育科学和技术联邦学院）
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Department of Multidisciplinary Engineering, Texas A&M University（多学科工程系，德克萨斯A&M大学）；Department of Computer Science, Ho Technical University（计算机科学系，霍技术大学）；Department of Computer Engineering, Kwame Nkrumah University of Science and Technology (KNUST)（计算机工程系，库马西科学与技术大学（KNUST））
PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning
Department of Electronics and Communication Engineering（电子与通信工程系）；Department of Mathematics and Computer Science（数学与计算机科学系）；Khulna University of Engineering & Technology（库尔纳工程与技术大学）；Hobart and William Smith Colleges（霍巴特和威廉·斯密斯学院）；Department of Electrical and Electronic Engineering（电气与电子工程系）
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Galician Research and Development Center in Advanced Telecommunications (GRADIANT)（加利西亚先进电信研究与发展中心（GRADIANT））；atlanTTic, Information & Computing Lab (ICLAB), Telecommunication Engineering School, Universidade de Vigo（atlanTTic，信息与计算实验室（ICLAB），电信工程学校，维戈大学）
Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smar…
Making AI-Assisted Grant Evaluation Auditable without Exposing the Model
Informatics Institute, Istanbul Technical University, Istanbul, Türkiye（伊斯坦布尔技术大学信息学院，伊斯坦布尔，土耳其）；Securify Information Technology and Security Training Consulting Inc., Ankara, Türkiye（Securify信息科技与安全培训咨询公司，安卡拉，土耳其）
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving de…
Unlearning for Federated Online Learning to Rank: A Reproducibility Study
Tsinghua University（清华大学）；South China University of Technology（华南理工大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Queensland（昆士兰大学）
