Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
Tsinghua University（清华大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；RWTH Aachen University（亚琛工业大学）；A*STAR（新加坡科技研究局）；Keio University（庆应义塾大学）；Technical University of Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek, Dan Li
The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.
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）
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）
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
Inner Mongolia University of Technology（内蒙古工业大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；New Jersey Institute of Technology（新泽西理工学院）
Decentralized Federated Learning: Balancing Communication and Computing Costs
分散式随机梯度下降（SGD）是分散式联邦学习（DFL）的驱动引擎。分散式SGD的性能受到节点间通信和本地更新的共同影响。在本文中，我们提出了一个通用的DFL框架，它实现了多个本地更新和多个节点间的通信周期，以达到通信效率和模型共识之间的平衡。它可以提供一个通用的去中心化SGD分析框架。我们为所提出的DFL算法建立了强收敛保证，而无需假设凸目标。DFL的收敛速度可以被优化，以实现在有限的资源下的通信和计算成本的平衡。为了提高DFL的通信效率，将压缩通信作为一种新的方案引入到DFL中，命名为DFL with compressed communication（C-DFL）。建议的C-DFL表现出线性收敛的强凸目标。基于MNIST和CIFAR-10数据集的实验结果表明，DFL优于传统的分散SGD方法，并表明C-DFL进一步提高了通信效率。
GFL: A Decentralized Federated Learning Framework Based On Blockchain
Polytechnic Institute, Zhejiang University（浙江大学理工学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University（浙江大学公共管理学院）
The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset
Georgia Institute of Technology（佐治亚理工学院）
联邦学习是一种适合在分散数据上训练模型的算法，但对中央“服务器”节点的要求是一个瓶颈。在本文中，我们首先介绍了分散式联邦学习（DFL）的概念。然后，我们在不同的设置上进行各种实验，例如改变模型聚合频率，从独立同分布（IID）数据集分区切换到部分全局共享的非IID分区，在客户端之间使用不同的优化方法，以及将模型分解为部分共享的片段。所有实验都在MNIST手写数字数据集上运行。我们观察到，这些改变的训练程序通常是健壮的，尽管不是最佳的。当模型权重之间的方差太大时，我们也观察到训练失败。开源实验代码可通过GitHub \footnote{Code was uploaded at \url{https://github.com/zhzhang2018/DecentralizedFL}}访问。
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
Communication Systems Group, Department of Informatics, University of Zurich UZH（苏黎世大学信息学院通信系统组）；Cyber-Defence Campus, armasuisse Science & Technology（armasuisse科学与技术网络）
Decentralized Federated Learning: A Survey on Security and Privacy
University of Windsor（温莎大学）；University of New Brunswick（新不伦瑞克大学）；University of Western Ontario（西安大略大学）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
Department of Electrical and Software Engineering, University of Calgary（电气与软件工程系，卡尔加里大学）；Department of Geomatics Engineering, University of Calgary（测绘工程系，卡尔加里大学）
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
National University of Defense Technology（国防科技大学）；Hebei University of Technology（河北工业大学）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）
