A Secure Federated Learning Framework for 5G Networks

Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from being involved in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

Introduction

Distributed machine learning has spawned a lot of useful applications, such as on-device learning and edge computing [1]. However, due to communication delays and network bandwidth limitations in fourth generation (4G) networks, mobile users holding smart devices with limited computing power cannot fully participate in distributed machine learning tasks. Fortunately, in the fifth generation (5G) networks, bottlenecks attributed to communication latency and network bandwidth will be overcome [2]. Therefore, attention can be focused on addressing the performance, and efficiency issues of distributed machine learning can be addressed. Consequently, mobile devices will be able to participate in distributed machine learning. However, traditional distributed machine learning techniques require a certain amount of private data to be aggregated and analyzed at central servers during the model training phase [3]. Such a training process would lead to potential privacy leakage for users in 5G networks [4], [5].