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Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it can discover insights behind real-world data, may not be feasible for many data-driven industry applications in light of such regulations. A new machine learning method, coined by Google as Federated Learning (FL) enables multiple participants to train a machine learning model collectively without directly exchanging data. However, recent studies have shown that there is still a possibility to exploit the shared models to extract personal or confidential data. In this paper, we propose to adopt Multi Party Computation (MPC) to achieve privacy-preserving model aggregation for FL. The MPC-enabled model aggregation in a peer-to-peer manner incurs high communication overhead with low scalability. To address this problem, the authors proposed to develop a two-phase mechanism by 1) electing a small committee and 2) providing MPC-enabled model aggregation service to a larger number of participants through the committee. The MPC enabled FL framework has been integrated in an IoT platform for smart manufacturing. It enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy, model accuracy vis-a-vis traditional machine learning methods and execution efficiency in terms of communication cost and execution time.
On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attack
Key Laboratory of High-Confidence \ Technologies (MOE) School of Computer Science Peking University Beijing China；Department of Computer Science, University of Illinois Urbana-Champaign Champaign Illinois USA；Department of Computer Science, Illinois Institute of Technology Chicago Illinois USA；Key Laboratory of High-Confidence \ Technologies (MOE) School of Computer Science Peking University；Department of Computer Science, University of Illinois Urbana-Champaign；Department of Computer Science, Illinois Institute of Technology
EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
Beijing Institute of Technology（北京理工大学）；School of Cyberspace Science and Technology（信息空间科学与技术学院）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE（教育部民族语言智能分析与安全治理重点实验室）；Minzu University of China（中央民族大学）；School of Information Engineering（信息工程学院）；School of Computing and Communications（计算与通信学院）
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
Quantum Cloud and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia（量子云与分布式系统实验室，计算机与信息系统学院，墨尔本大学，澳大利亚）；Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering, Bangalore University, India（计算机科学与工程系，维萨瓦拉亚工程学院，班加罗尔大学，印度）
FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）；Department of Statistics and Actuarial Science, University of Waterloo（滑铁卢大学统计与精算科学系）
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）；National Mobile Communications Research Laboratory（移动通信国家重点实验室）；Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；George Washington University（乔治华盛顿大学）；Ryerson University（瑞尔森大学）
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Ecole de Technologie Superieure (ETS)（高等技术学院）；Khalifa University（哈利法大学）；Universite de Quebec a Trois-Rivieres (UQTR)（魁北克大学三河分校）；Lebanese American University（黎巴嫩美国大学）；New York University（纽约大学）
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学信息科学与技术国家研究中心电子工程系）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信系统国家重点实验室和电信工程学院）；Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；Department of Electrical and Computer Engineering, the Hong Kong University of Science and Technology (HKUST)（香港科技大学电子与计算机工程系）
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
University of Helsinki（赫尔辛基大学）；University of Technology Sydney（悉尼科技大学）；Beihang University（北京航空航天大学）；Monash University（莫纳什大学）；Aalto University（阿尔托大学）；Griffith University（格里菲斯大学）；Amazon（亚马逊公司）；Columbia University（哥伦比亚大学）
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Centre of Informatics and Systems of the University of Coimbra, CISUC（科英布拉大学信息与系统中心）；University of Coimbra（科英布拉大学）；Institute for Systems Engineering and Computers, INESC（系统工程与计算机研究所）；Sul-Rio-Grandense Federal Institute (IFSUL)（南里奥格兰德联邦学院）；Instituto de Telecomunicações（电信研究所）；Polytechnic Institute of Coimbra（科英布拉理工学院）；Coimbra Business School（科英布拉商学院）；INESC Coimbra（科英布拉INESC）
