Robust Decentralized Online Learning against Malicious Data Generators and Dynamic Feedback Delays with Application to Traffic Classification

Motivated by the real-world application of traffic classification at the network edge, we study the problem of robust decentralized online learning against malicious data generators that can manipulate their data features with an aim to gain preferred classification outcomes. Multiple agents cooperatively learn classification models to make online decisions. They periodically exchange their models, e.g., traffic classification models, between neighbors in a decentralized network and update local model parameters on the fly based on the models they have access to and feedback on the observed local data samples that are dynamically delayed. In this work, we propose two decentralized online learning algorithms, RDOC-O and RDOC-C, respectively against ordinary malicious and clairvoyant malicious data generators. Our theoretical performance analysis shows that the two algorithms have provable sub-linear individual regret bounds under mild conditions. To validate our analysis, extensive performance evaluations are conducted in the application of network traffic classification using two real-world data traces. Our results show that the two proposed algorithms compare favorably with an optimal offline classification model in the presence of malicious data generators, and they can achieve a steady-state F₁ score of around 0.85, which validates their effectiveness and makes them appealing in practice.