Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy. However, the aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot manage clients' behavior. Therefore, the global model's performance and convergence of the training process will be affected under such attacks.To mitigate this vulnerability issue, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing via incorporating the worker's reliability into aggregation. We evaluate our solution on three real-world datasets with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning and is resilient to various types of attacks, including noisy data attacks, Byzantine attacks, and label flipping attacks.
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Department of Computer Science, Virginia Tech（维吉尼亚理工大学计算机科学系）；US DEVCOM Army Research Laboratory（美国陆军研发实验室）；Department of Computer Science, Old Dominion University（旧 Dominion 大学计算机科学系）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）
Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach
National Natural Science Foundation of China（国家自然科学基金委员会）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Applied Basic Research Foundation of Yunnan Province（云南省应用基础研究基金）；Yunnan Provincial Department of Education Science Research Foundation（云南省教育厅科学技术研究基金）；Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics（云南财经大学服务计算重点实验室）；Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, China（教育部数字学习技术整合与应用工程研究中心）；Engineering Research Center of Cyberspace, Ministry of Education（教育部网络空间工程研究中心）；National Pilot School of Software, Yunnan University（云南大学软件国家试点学院）
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Carnegie Mellon University（卡内基梅隆大学）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Petuum Inc.（Petuum公司）
联邦学习的规范公式将其视为分布式优化问题，其中模型参数针对全局损失函数进行优化，该全局损失函数在客户端损失函数之间进行分解。最近的替代公式将联邦学习视为分布式推理问题，其中目标是从分区的客户端数据推断全局后验（Al-Shedivat et al.，2021年）。本文扩展了推理视图，并描述了变分推理公式的联邦学习的目标是找到一个全局变分后验，以及近似的真实后验。这自然激发了联邦学习（FedEP）的期望传播方法，其中通过中央服务器和客户端之间的概率消息传递迭代地细化全局后验的近似值。我们进行了广泛的实证研究，在各种算法的考虑，并描述了实际的战略，扩大期望传播到现代联邦设置。我们将FedEP应用于标准的联邦学习基准测试，发现它在收敛速度和准确性方面都优于强基线。
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
Department of Electrical Engineering and Computer Science, University of Stavanger（电子工程与计算机科学系，斯塔万格大学）；Department of Data Science, The Catholic University of Korea（数据科学系，韩国天主大学）
Improving Local Training in Federated Learning via Temperature Scaling
School of Integrated Technology, Yonsei University（燕尼大学整合技术学院）；BK21 Graduate Program in Intelligent Semiconductor Technology, Yonsei University（燕尼大学智能半导体技术研究生项目）；Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气工程与计算机科学系）
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Guangdong University of Technology（广东工业大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Tongji University（同济大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
University of Warwick, Department of Computer Science, Coventry, United Kingdom（沃里克大学计算机科学系）；University of Warwick, Department of Statistics, Coventry, United Kingdom（沃里克大学统计系）
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
Communication Systems Group, Department of Informatics, University of Zurich, CH--8050 Zürich, Switzerland（苏黎世大学信息学院通信系统组）；Department of Information and Communications Engineering, University of Murcia, 30100--Murcia, Spain（穆尔西亚大学信息与通信工程系）
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）
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
Department of Electrical Engineering, Indian Institute of Technology Madras（印度理工学院马德拉斯分校电子工程系）；Department of Computer Science and Engineering, Jadavpur University（贾达布尔大学计算机科学与工程系）
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Technische Universität Wien（维也纳技术大学）；CSC-CONICET（阿根廷国家科学技术研究委员会）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；MILA - Quebec AI Institute（魁北克人工智能研究所）；CNRS（法国国家科学研究中心）；CentraleSupélec（中央高等电力学院）
