标题： 联邦学习中具有压缩的脉冲神经网络对非全知拜占庭攻击的鲁棒性 显示英文标题

标题： The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks

摘要： 脉冲神经网络（SNNs），其在推理方面具有出色的能效，以及联邦学习（FL），其提供隐私保护的分布式训练，是一个日益受到关注的领域，对物联网（IoT）设备具有高度的益处。 尽管如此，针对FL-SNNs中的拜占庭攻击和带宽限制的研究，这两者都对模型收敛和训练时间构成重大威胁，仍然大多未被探索。 除了提出解决这两个问题的方案外，在本工作中，我们强调了FL-SNNs的双重优势，相对于非全知的拜占庭对手（那些限制攻击者访问本地客户端数据集的对手），以及更高的通信效率，优于FL-ANNs。 具体来说，我们发现将Top-\k{appa}稀疏化简单地集成到FL框架中，可以帮助利用SNN模型的优势，在大大减少带宽使用量和显著提高FL训练 against 非全知拜占庭对手的鲁棒性方面。 最值得注意的是，我们在致命的MinMax攻击下，FL-SNNs训练中看到了大约40%的准确率提升。 显示英文摘要

摘要： Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices. Despite this, research that tackles Byzantine attacks and bandwidth limitation in FL-SNNs, both poses significant threats on model convergence and training times, still remains largely unexplored. Going beyond proposing a solution for both of these problems, in this work we highlight the dual benefits of FL-SNNs, against non-omniscient Byzantine adversaries (ones that restrict attackers access to local clients datasets), and greater communication efficiency, over FL-ANNs. Specifically, we discovered that a simple integration of Top-\k{appa} sparsification into the FL apparatus can help leverage the advantages of the SNN models in both greatly reducing bandwidth usage and significantly boosting the robustness of FL training against non-omniscient Byzantine adversaries. Most notably, we saw a massive improvement of roughly 40% accuracy gain in FL-SNNs training under the lethal MinMax attack