Machine Learning has proven useful in the recent years as a way to achieve failure prediction for industrial systems. However, the high computational resources necessary to run learning algorithms are an obstacle to its widespread application. The sub-field of Distributed Learning offers a solution to this problem by enabling the use of remote resources but at the expense of introducing communication costs in the application that are not always acceptable. In this paper, we propose a distributed learning approach able to optimize the use of computational and communication resources to achieve excellent learning model performances through a centralized architecture. To achieve this, we present a new centralized distributed learning algorithm that relies on the learning paradigms of Active Learning and Federated Learning to offer a communication-efficient method that offers guarantees of model precision on both the clients and the central server. We evaluate this method on a public benchmark and show that its performances in terms of precision are very close to state-of-the-art performance level of non-distributed learning despite additional constraints.
FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；VinUni-Illinois Smart Health Center, VinUniversity（VinUniversity-VinUni-Illinois智能健康中心）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）；Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院）
FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur（印度焦特布尔印度理工学院人工智能与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur（印度焦特布尔印度理工学院计算机科学与工程系）；Laboratoire d’Informatique de Grenoble (LIG), University of Grenoble Alpes（格勒诺布尔阿尔卑斯大学格勒诺布尔计算机实验室）
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Chainbase Inc.（Chainbase公司）；PublicAI Inc.（PublicAI公司）
Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities
National Research Council, Pisa（比萨国家研究委员会）；Central European University, Vienna（维也纳中欧大学）；Aalto University, Espoo（埃斯波阿尔托大学）；HUN-REN Rényi Institute of Mathematics, Budapest（布达佩斯匈牙利科学院雷尼数学研究所）
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets
Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）；Engineering School of São Carlos（圣卡洛斯工程学院）；University of São Paulo（圣保罗大学）；Federal University of São Carlos（圣卡洛斯联邦大学）；Italian Institute of Technology（意大利理工学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle’s Illinois College of Medicine（卡尔伊利诺伊医学院）；GE Healthcare（GE医疗）；University of Chicago（芝加哥大学）
