Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
Shanghai Jiao Tong University（上海交通大学）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Queen’s University Belfast（贝尔法斯特女王大学）；Stevens Institute of Technology（史蒂文斯理工学院）
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Weiyang College, Tsinghua University（清华大学未央书院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；College of Arts and Sciences, The University of Tokyo（东京大学文理学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
University of Southern California（南加州大学）；Emory University（埃默里大学）；Lehigh University（理海大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Tencent AI Lab（腾讯AI实验室）
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
Morfix；OpenMined；École Supérieure en Informatique, Sidi Bel Abbès（西迪贝勒阿巴斯高等计算机学院）；Bitdefender（比特梵德）；Edinburgh Napier University（爱丁堡龙比亚大学）；apheris AI；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Tessella
ByzFL: Research Framework for Robust Federated Learning
Universitat Politècnica de Catalunya (UPC)（巴塞罗那技术大学）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Sorbonne Université, Université Paris Cité, CNRS（索邦大学、巴黎cité大学、国家科学研究中心）；Université de Rennes, Inria, CNRS, IRISA（雷恩大学、法国国家科学研究中心、IRISA）
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
The University of Iowa（爱荷华大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；State University of New York at Albany（纽约州立大学奥尔巴尼分校）
深度AUC（area under the ROC curve）最大化（DAM）方法由于其在不平衡数据分类中的巨大潜力而受到广泛关注。然而，联邦深度AUC最大化（FDAM）的研究仍然有限。与关注可分解最小化目标的标准联邦学习（FL）方法相比，FDAM由于其最小化目标在单个示例上不可分解而更加复杂。在本文中，我们提出了改进的FDAM算法的异构数据，通过解决流行的非凸强凹的min-max制定的DAM在分布式的方式，这也可以应用到一类非凸强凹min-max问题。本文的一个引人注目的结果是，所提出的算法的通信复杂性是一个常数，独立于机器的数量，也独立于精度水平，这改善了一个现有的结果的数量级。实验证明了我们的FDAM算法的有效性基准数据集上，并从不同组织的医学胸部X射线图像。我们的实验表明，使用来自多个医院的数据的FDAM的性能可以提高来自单个医院的测试数据的AUC分数，用于基于胸片检测危及生命的疾病。所提出的方法在我们的开源库LibAUC（www.libauc.org）中实现，其github地址为https://github.com/Optimization-AI/ICML2021_FedDeepAUC_CODASCA。
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Institute of Biomedical Engineering（生物医学工程研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Department of Political Science（政治学系）；Università Degli Studi Di Teramo（特拉莫大学）；Department of Radiation Oncology（放射肿瘤学系）；University Medical Center Schleswig-Holstein（什未林施瓦茨-霍斯特大学医院）；Department of Experimental and Clinical Medicine（实验与临床医学系）；Magna Graecia University（马格拉西亚大学）
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing software with a webcam
University College London（伦敦大学学院）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）；Microsoft UK（微软英国）
