Do Not Perturb Me: A Secure Byzantine-Robust Mechanism for Machine Learning in IoT

With the development of Internet of Things (IoTs) and big data, collaborative machine learning has achieved many impressive successes in IoT to improve the system performance and provide more diversified services for people. Although one of the motivations for collaborative learning is privacy preservation, the adversary can still launch an inference attack through task nodes' shared information. What's worse, a few task nodes may perform as a Byzantine attacker to compromise the entire system. Many Byzantine-robust mechanisms have been proposed, but they relied on outsourcing the calculation on two non-colluding servers which were not realistic in practice or had privacy issues in one-server architecture. In this paper, we design a novel mechanism for secure Byzantine-robust collaborative machine learning, namely Omega, to allow IoT devices to achieve the collaborative model training without exposing their local data to the others. Specifically, we construct a single-server architecture to achieve the private aggregation of parameter gradients, which protects task nodes' local data even n-1 of n nodes colluded. A new secure Byzantine-robust protocol is also designed to resist the Byzantine attack and this protocol can be extended to any distance-based robust rule. Furthermore, we prove that Omega can ensure task nodes' privacy preservation. Finally, we conduct an experiment to evaluate Omega over real-world dataset and empirical results demonstrate that Omega can efficiently achieve the collaborative machine learning.

I. Introduction

With the development of Internet of Thing (IoT) and big data, machine learning has achieved many impressive successes in IoT applications to improve the system performance and provide more diversified services for people. Nowadays, the architectures of machine learning tend to aggregate several data owners to achieve the collaborative learning, including the distributed learning and federated learning. Together with the greatly enhanced computation capability of IoT devices, collaborative machine learning (termed as federated learning as well) has emerged as a vastly-developed learning scheme in the real world IoT application. In collaborative learning, each task node owns its local training data and trains its local model. Then, all task nodes periodically exchange their trained gradients and update the global model with or without a central server.