Efficient Distributed Swarm Learning for Edge Computing
Efficient Distributed Swarm Learning for Edge Computing
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DOI：
10.1109/icc45041.2023.10279508
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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
中科院分区：
文献类型：
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作者：
Xin Fan;Yue Wang;Yan Huo;Zhi Tian
联合学习(FL)方法在EDGE物联网场景中面临着通信瓶颈、数据异构性和安全问题等重大挑战。受群居生物生物智能(BI)研究的成功启发，通过将人工智能支持的随机梯度下降和BI支持的粒子群优化相结合，提出了一种新的群物联网边缘学习方法，称为通信高效和拜占庭稳健的分布式群学习(CB-DSL)。处理非独立和同分布(非I.I.D.)针对数据问题和拜占庭攻击，CB-DSL引入了非常少量的全局数据样本，并在物联网工作人员之间进行共享，不仅有效地缓解了局部数据的异构性，而且使群体智能的探索开发机制得到了充分的利用。此外，我们进行了收敛分析，从理论上证明了所提出的CB-DSL算法优于标准FL算法，具有更好的收敛性能。此外，为了衡量引入全局共享数据集的有效性，我们还通过推导模型的上界来评估模型的发散性。仿真结果表明，本文提出的CB-DSL在更快的收敛速度、更高的收敛精度、更低的通信代价和更好的抗非I.I.D.攻击能力方面都优于已有的基准。数据和拜占庭攻击11我们的代码可以在：https://github.com/fuanxiyin/CB-DSL.git..上找到
Federated learning (FL) methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios. In this paper, inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-independent and identically distributed (non-i.i.d.) data issues and Byzantine attacks, a very small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks11Our code can be found at:https://github.com/fuanxiyin/CB-DSL.git..