Robust Distributed Swarm Learning for Intelligent IoT
Robust Distributed Swarm Learning for Intelligent IoT
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DOI：
10.1109/icc45041.2023.10278708
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Xin Fan;Yue Wang;Yan Huo;Zhi Tian
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文献类型：
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作者：
Xin Fan;Yue Wang;Yan Huo;Zhi Tian
在本文中，我们研究了一种通信高效的分布式学习方案，通过联邦学习（FL）和粒子群优化的整体集成，称为DSL，这是适合智能物联网应用的实现。由于只有一个选择的最佳从所有的本地设备需要报告其本地模型更新的参数服务器，DSL的通信成本大大降低相比，其对应的标准FL。然而，DSL是容易受到敌对攻击者。为了实现拜占庭弹性DSL，我们建议引入一个共享数据集，用于对屏幕攻击者的本地更新进行评分。我们进一步提供的收敛性分析，从理论上证明CB-DSL是上级比标准FL。实验结果表明，我们提出的CB-DSL的学习性能优于现有的基准只有少量的全球共享的数据。它比vanilla DSL具有更高的抗拜占庭攻击的鲁棒性，并且比标准FL 11具有更好的通信效率。https://github.com/fuanxiyin/CB-DSL.git
In this paper, we study a communication-efficient distributed learning scheme through a holistic integration of federated learning (FL) and particle swarm optimization, called DSL, which is suitable for the implementation of intelligent IoT applications. Since only one selected optimum from all local devices need to report its local model updates to the parameter server, the communication cost of DSL is much reduced compared to its counterpart of standard FL. However, the DSL is vulnerable to adversarial attackers. To achieve Byzantine-resilient DSL, we propose to introduce a shared dataset for scoring local updates to screen attackers. We further provide the convergence analysis to theoretically demonstrate that CB-DSL is superior than the standard FL. Experiment results show that the learning performance of our proposed CB-DSL outperforms the existing benchmarks with only a small amount of globally shared data. It enjoys higher robustness against Byzantine attacks than the vanilla DSL, and has better communication efficiency than the standard FL11Our code can be found at: https://github.com/fuanxiyin/CB-DSL.git..