Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Abstract

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

Keywords

• Federated learning
• Federated learning threats and attacks
• Federated learning security solutions
• Internet of things
• Machine learning security