【摘要】 联邦学习使得多个移动参与者在不透露其本地隐私数据的情况下联合训练全局模型。通信计算代价和隐私保护性能是联邦学习的关键基础问题。现有的基于秘密共享的联邦学习安全聚合机制仍然存在引入显著额外计算代价、隐私保护性能不足和应对参与者掉线能力脆弱等问题。本文致力于通过引入灵活高效的秘密共享机制解决上述问题。本文提出了两种新颖的隐私保护联邦学习方案，分别是基于单向秘密共享的联邦学习（FLOSS）和基于多发秘密共享的联邦学习(FLMSS)。与当前的相关工作相比，FLOSS通过动态设计秘密共享的内容和对象，在显著降低通信代价的同时保证高强度的隐私保护性能。FLMSS进一步降低额外计算代价并且能够提高联邦学习应对参与者掉线的鲁棒性，从而在隐私保护和通信计算代价之间取得令人满意的平衡。安全性分析和基于真实数据集的性能评估证明了本文提出的方案在模型准确度、隐私保护性能和通信计算代价方面的优势。更多还原

【Abstract】 Federated learning allows multiple mobile participants to jointly train a global model without revealing their local private data. Communication-computation cost and privacy preservation are key fundamental issues in federated learning. Existing secret sharing-based secure aggregation mechanisms for federated learning still suffer from significant additional costs, insufficient privacy preservation, and vulnerability to participant dropouts. In this paper, we aim to solve these issues by introducing flexible and effective secret sharing mechanisms into federated learning. We propose two novel privacy-preserving federated learning schemes: federated learning based on one-way secret sharing(FLOSS) and federated learning based on multi-shot secret sharing(FLMSS). Compared with the state-of-the-art works, FLOSS enables high privacy preservation while significantly reducing the communication cost by dynamically designing secretly shared content and objects. Meanwhile, FLMSS further reduces the additional cost and has the ability to efficiently enhance the robustness of participant dropouts in federated learning. Foremost, FLMSS achieves a satisfactory tradeoff between privacy preservation and communication-computation cost. Security analysis and performance evaluations on real datasets demonstrate the superiority of our proposed schemes in terms of model accuracy, privacy preservation, and cost reduction.更多还原