论文标题
联合学习：平衡数据智能与隐私之间的细线
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy
论文作者
论文摘要
联邦学习在从碎片敏感数据中学习方面具有巨大的希望，并彻底改变了机器学习模型的训练方式。本文提供了联合学习的系统概述和详细的分类学。我们调查了联邦学习中现有的安全挑战，并提供了有关数据中毒，推理攻击和模型中毒攻击的既定防御技术的全面概述。这项工作还概述了当前的联邦学习培训挑战，重点是处理非i.i.d。数据，高维问题和异质体系结构，并讨论了有关相关挑战的几种解决方案。最后，我们讨论了管理联合学习培训的剩余挑战，并提出了重点的研究方向以解决开放问题。讨论了联合学习的潜在候选领域，包括物联网生态系统，医疗保健应用，特别关注银行和金融领域。
Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic overview and detailed taxonomy of federated learning. We investigate the existing security challenges in federated learning and provide a comprehensive overview of established defense techniques for data poisoning, inference attacks, and model poisoning attacks. The work also presents an overview of current training challenges for federated learning, focusing on handling non-i.i.d. data, high dimensionality issues, and heterogeneous architecture, and discusses several solutions for the associated challenges. Finally, we discuss the remaining challenges in managing federated learning training and suggest focused research directions to address the open questions. Potential candidate areas for federated learning, including IoT ecosystem, healthcare applications, are discussed with a particular focus on banking and financial domains.
