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Privacy-preserving and communication-efficient federated learning in Internet of Things
Computers & Security ( IF 5.6 ) Pub Date : 2021-01-15 , DOI: 10.1016/j.cose.2021.102199
Chen Fang , Yuanbo Guo , Yongjin Hu , Bowen Ma , Li Feng , Anqi Yin

Aimed at the privacy leakage caused by collecting data from numerous Internet of Things (IoT) devices for centralized training, a novel distributed learning framework, namely federated learning, came into being, where devices train models collaboratively while leaving their private datasets locally. Although many schemes have been proposed about federated learning, they are still short in communications and privacy due to the limited network bandwidth and advanced privacy attacks. To address these challenges, we develop PCFL, a privacy-preserving and communication-efficient scheme for federated learning in IoT. PCFL is composed of three key components: (1) gradient spatial sparsification where irrelevant local updates that deviate from the collaborative convergence tendency are prevented from being uploaded; (2) bidirectional compression where computation-less compression operators are used to quantize the gradients both at the device-side and server-side; and (3) privacy-preserving protocol which integrates secret sharing with lightweight homomorphic encryption to protect the data privacy and resist against various collusion scenarios. We analyze the correctness and privacy of our scheme, and carry out theoretical and experimental comparison on two real-world datasets. Results show that PCFL outperforms the state-of-the-art methods by more than 2× in terms of communication efficiency, along with high model accuracy and marginal decreases in convergence rate.



中文翻译:

物联网中保护隐私和通信效率的联合学习

针对从众多物联网(IoT)设备收集数据进行集中培训而导致的隐私泄漏,应运而生了一种新颖的分布式学习框架,即联合学习,其中设备可以协同训练模型,而将其私有数据集保留在本地。尽管已经提出了许多有关联合学习的方案,但是由于网络带宽有限和高级隐私攻击,它们在通信和隐私方面仍然很不足。为了应对这些挑战,我们开发了PCFL,这是一种用于IoT中联合学习的隐私保护和通信高效方案。PCFL由三个关键部分组成:(1)梯度空间稀疏化,可以防止上传偏离协作收敛趋势的不相关本地更新;(2)双向压缩,其中使用较少计算的压缩运算符来量化设备端和服务器端的梯度;(3)隐私保护协议,该协议将秘密共享与轻量级的同态加密相集成,以保护数据隐私并抵御各种串通情况。我们分析了该方案的正确性和保密性,并对两个真实世界的数据集进行了理论和实验比较。结果表明,PCFL的性能比最先进的方法高出2倍以上 我们分析了该方案的正确性和保密性,并对两个真实世界的数据集进行了理论和实验比较。结果表明,PCFL的性能比最先进的方法高出2倍以上 我们分析了该方案的正确性和保密性,并对两个真实世界的数据集进行了理论和实验比较。结果表明,PCFL的性能比最先进的方法高出2倍以上× 在通信效率方面,伴随着较高的模型精度和收敛速度的小幅下降。

更新日期:2021-01-28
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