在无线网络中对基于区块链的分散式联合学习进行破坏性干扰的威胁
1Agency for Defense Development, Daejeon 34186, Republic of Korea.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
在无线网络中使用区块链进行联合学习,面临着干扰威胁. 恶意矿工可以破坏正常操作,影响模型更新和系统完整性.
科学领域:
- 无线通信网络是无线通信网络.
- 分布式机器学习 分布式机器学习
- 物联网中的网络安全
背景情况:
- 无线网络中的机器学习提供了高性能,但面临隐私和通信挑战.
- 联合学习 (FL) 通过共享模型更新而不是原始数据来解决这些问题.
- 与FL (BDFL) 的区块链集成使得没有中央服务器的分散学习成为可能,但仍然容易受到攻击.
研究的目的:
- 分析来自恶意矿工的干扰威胁对无线网络中基于区块链的去中心化联合学习 (BDFL) 的影响.
- 了解干扰如何影响BDFL系统的完整性和性能.
- 评估BDFL对复杂的对抗性攻击的有效性.
主要方法:
- 模拟了一个无线BDFL系统,包含具有干扰能力的恶意矿工.
- 分析了干扰对从正常矿工收集模型参数的干扰.
- 评估了恶意区块插入的成功概率和正常矿工的参与率.
主要成果:
- 带有干扰的恶意矿工可以有效地破坏全球模型生成过程.
- 干扰使恶意行为者能够更容易地将有害数据注入到学习模型中.
- 该研究量化了对正常矿工参与和成功攻击的可能性的影响.
结论:
- 干扰对无线BDFL系统的安全性和可靠性构成重大威胁.
- 敌对攻击可以降低BDFL的性能并损害数据完整性.
- 需要进一步的研究,以开发在分散的学习环境中对这种威胁的强有力的防御机制.
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