Federated Learning (FL) is a privacy-preserving distributed deep learning paradigm that involves substantial communication and computation effort, which is a problem for resource-constrained mobile and IoT devices. Model pruning/sparsification develops sparse models that could solve this problem, but existing sparsification solutions cannot satisfy at the same time the requirements for low bidirectional communication overhead between the server and the clients, low computation overhead at the clients, and good model accuracy, under the FL assumption that the server does not have access to raw data to fine-tune the pruned models. We propose Complement Sparsification (CS), a pruning mechanism that satisfies all these requirements through a complementary and collaborative pruning done at the server and the clients. At each round, CS creates a global sparse model that contains the weights that capture the general data distribution of all clients, while the clients create local sparse models with the weights pruned from the global model to capture the local trends. For improved model performance, these two types of complementary sparse models are aggregated into a dense model in each round, which is subsequently pruned in an iterative process. CS requires little computation overhead on the top of vanilla FL for both the server and the clients. We demonstrate that CS is an approximation of vanilla FL and, thus, its models perform well. We evaluate CS experimentally with two popular FL benchmark datasets. CS achieves substantial reduction in bidirectional communication, while achieving performance comparable with vanilla FL. In addition, CS outperforms baseline pruning mechanisms for FL.
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
University of Science and Technology of China（中国科学技术大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Exeter（埃克塞特大学）；Michigan State University（密歇根州立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；MIT（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Harvard University（哈佛大学）
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；Stevens Neuroimaging and Informatics Institute, University of Southern California（南加州大学史蒂文斯神经影像与信息学研究所）
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yale University（耶鲁大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Stanford Institute for Human-Centered Artificial Intelligence（斯坦福人类中心人工智能研究所）；Imperial College London（帝国理工学院）
Efficient Asynchronous Federated Learning with Sparsification and Quantization
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Hithink RoyalFlush Information Network Co., Ltd.（同花顺信息服务股份有限公司）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术及产业化协同创新中心）；Department of Sciences and Informatics, Muroran Institute of Technology（室兰工业大学科学与信息学系）；Boston Consulting Group（波士顿咨询公司）
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Applied AI（应用人工智能）；Artificial Intelligence Research Institute（人工智能研究所）；HSE University（俄罗斯高等经济大学）；Sb AI Lab（Sb人工智能实验室）；MWS AI（MWS人工智能）；Yandex；Independent Researcher（独立研究者）
Mix-and-Match Pruning: Globally Guided Layer-Wise Sparsification of DNNs
1 Brandenburg Technical University, Cottbus, Germany；2 University of Zanjan, Iran；3 Tallinn University of Technology, Estonia；4 Leibniz Institute for High Performance Microelectronics, Frankfurt Oder, Germany
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University（北京信息科学与技术国家研究中心，电子工程系，清华大学）；School of Electronic and Information Engineering, Beijing Jiaotong University（电子与信息工程学院，北京交通大学）
Differentially Private Federated Learning with Local Regularization and Sparsification
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
Hong Kong University of Science and Technology（香港科技大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Southern University of Science and Technology（南方科技大学）
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Joint NTU-Webank Research Institute on Fintech, Nanyang Technological University（南洋理工大学-微众银行金融科技联合研究院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University（南洋理工大学-英属哥伦比亚大学老年人积极生活联合卓越研究中心）
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Institute of Communications Engineering, National Tsing Hua University（国立清华大学通信工程研究所）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Yonsei University（延世大学）
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Cyber Science and Technology at Beihang University（北京航空航天大学网络安全科学与技术学院）；School of Cyberspace Science and Technology, Beijing Jiaotong University（北京交通大学网络空间科学与技术学院）；Beijing Key Laboratory of Security and Privacy in Intelligent Transportation（北京智能交通安全与隐私重点实验室）；Zhongguancun Laboratory（中关村实验室）；School of Computing and Information,University of Pittsburgh（匹兹堡大学计算与信息学院）
Exploring Federated Pruning for Large Language Models
The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；Sun Yat-sen University（中山大学）；Guangming Laboratory（光明实验室）；Hangzhou Dianzi University（杭州电子科技大学）
