A survey on secure decentralized optimization and learning
KTH Royal Institute of Technology（KTH皇家理工学院）；Digital Futures（数字未来机构）；Hong Kong University of Science and Technology（香港科技大学）；University of Victoria（维多利亚大学）
Changxin Liu, Nicola Bastianello, Wei Huo, Yang Shi, Karl H. Johansson
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions.
Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience
Department of Electrical Engineering and Computer Science, Technical University of Berlin（技术大学柏林电气工程与计算机科学系）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University（广西多媒体通信与网络技术重点实验室，广西大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电子工程与电信学院）
Department of Computer Science, University of Oxford, Oxford, UK；Newcastle University, UK；Hong Kong University of Science；HongKong Generative AI Research \& Development Center, Hong Kong SAR
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
Hong Kong University of Science and Technology（香港科技大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Southern California（南加州大学）
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
University of Tokyo（东京大学）
更广泛的覆盖范围和更好的解决方案，以减少5G的延迟，需要它与多路访问边缘计算（MEC）技术相结合。分散式深度学习（Decentralized Deep Learning，简称DAI），如联邦学习（Federated Learning）和群学习（Swarm Learning），作为数百万智能边缘设备的隐私保护数据处理的一种有前途的解决方案，利用了本地客户端网络内多层神经网络的分布式计算，而不会泄露原始的本地训练数据。值得注意的是，在金融和医疗保健等行业，交易和个人医疗记录的敏感数据被谨慎地保存着，ESTA可以促进这些机构之间的合作，以提高训练模型的性能，同时保护参与客户的数据隐私。在这篇调查论文中，我们展示了分布式学习的技术基础，这些技术基础通过分散式学习使社会各界受益。此外，我们还从通信效率和可信度的新角度概述了物联网的挑战和最相关的解决方案，从而全面概述了该领域的当前最新技术水平。
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System
University of Southern California（南加州大学）
分散学习是一种有效的新兴范式，用于提高多个有界计算代理的计算能力。在大数据时代，在分布式和联邦学习（DL和FL）框架内执行推理，中央服务器需要处理大量数据，同时依赖各种代理执行多个分布式训练任务。考虑到分布式计算的拓扑结构，隐私已经成为一个首要的问题。此外，假设代理的信息处理能力有限，需要一个复杂的\textit{privacy-preserving decentralization}来确保有效的计算。为此，我们研究的\textit{privacy-aware server to multi-agent assignment}问题受到信息处理的限制与每个代理，同时保持隐私和确保学习信息的消息，通过分布式私人联邦学习（DPFL）的方法收到代理全球终端。为了找到一个分散的计划，两个代理系统，我们制定了一个优化问题，平衡隐私和准确性，考虑到与每个代理相关联的压缩约束的质量。我们提出了一个迭代收敛算法交替自洽方程。我们还对所提出的解决方案进行了数值评估，以显示隐私预测的权衡，并证明了新方法在确保DL和FL隐私方面的有效性。
Department of Computer, Control and Management Engineering, Sapienza University of Rome（罗马萨皮恩扎大学计算机、控制与管理工程系）；Department of Computer Science, Sapienza University of Rome（罗马萨皮恩扎大学计算机科学系）；Università della Svizzera Italiana（意大利瑞士大学）
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）
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）；Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China（电子科技大学长三角研究院（湖州））；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）
