The gossip-based distributed projected subgradient algorithms (DPS) are widely used to solve decentralized optimization problems in various multi-agent applications, while they are generally vulnerable to data injection attacks by internal malicious agents as each agent locally estimates its descent direction without an authorized supervision. In this work, we explore the application of artificial intelligence (AI) technologies to detect internal attacks. We show that a general neural network is particularly suitable for detecting and localizing the malicious agents, as they can effectively explore nonlinear relationship underlying the collected data. Moreover, we propose to adopt one of the state-of-the-art approaches in decentralized federated learning, i.e., a gossip-based collaborative learning protocol, to facilitate training the neural network models by gossip exchanges. This advanced approach is expected to make our model more robust to challenges with insufficient training data, or mismatched test data. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods. Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.

中文翻译：

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分布式投影次梯度算法中的内部攻击检测


基于八卦的分布式投影次梯度算法（DPS）广泛用于解决各种多智能体应用中的去中心化优化问题，但由于每个智能体在未经授权的情况下在本地估计其下降方向，因此它们通常容易受到内部恶意智能体的数据注入攻击监督。在这项工作中，我们探索应用人工智能（AI）技术来检测内部攻击。我们表明，通用神经网络特别适合检测和定位恶意代理，因为它们可以有效地探索收集数据背后的非线性关系。此外，我们建议采用去中心化联邦学习中最先进的方法之一，即基于八卦的协作学习协议，以促进通过八卦交换来训练神经网络模型。这种先进的方法预计将使我们的模型更加稳健，以应对训练数据不足或测试数据不匹配的挑战。在我们的模拟中，考虑最小二乘问题来验证基于人工智能的方法的可行性和有效性。仿真结果表明，与基于分数的方法相比，所提出的基于人工智能的方法有利于提高检测和定位恶意代理的性能，并且点对点神经网络模型确实对目标问题具有鲁棒性。