Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review
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Abstract (arXiv)
Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures -- specifically within the context of IoT -- using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 70 references that are in the map. First posted on arXiv 25 Jul 2024.
Builds on
12 of 70 references.
References · 12
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
- 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
- 2021Decentralized Federated Averagingcited by 114
- 2022Gradient Obfuscation Gives a False Sense of Security in Federated Learningcited by 39
- 2020VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoTcited by 18
- 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
- 2020Privacy Preserving Distributed Machine Learning with Federated Learningcited by 13
- 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
- 2020Mitigating Sybil Attacks on Differential Privacy based Federated Learningcited by 5
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2 papers.