Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

By Latif Ullah Khan, Walid Saad, Zhu Han, Ekram Hossain, Choong Seon Hong. IEEE Communications Surveys and Tutorials, 2020

798 citations recorded by the source corpus.

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Finally, we present several open research challenges with their possible solutions.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/29e6b12d3c6cd55e04bdfb9c22201f99578f4080

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Citations in the corpus

Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “[100] They present advances in federated learning for IoT applications and provide a taxonomy using various parameters.”
Federated Learning for Smart Healthcare: A Survey (2021, background): “Meanwhile, the convergence of FL and Internet-of-Things (IoT) is explored in [15], by providing a survey on the technical issues in FL designs, such as sparsification, robustness, privacy, and scalability, along with a brief discussion of FL applications in IoT.”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, neutral): “In this sense, in 2020, Lim et al. [23] reviewed the use of CFL in mobile networks using different end-user applications, while in 2021, researchers such as Nguyen et al. [24] and Khan et al. [25] examined the application of FL in more complex scenarios involving different heterogeneous IoT devices.”
Asynchronous Federated Learning on Heterogeneous Devices: A Survey (2021, methodology): “[40] FL on IoT Explain the concept of AFL without comparing different AFL schemes.”
Decentralized Federated Learning: A Survey and Perspective (2023, neutral): “…its excellent capabilities in various areas, including intelligent transportation, Internet of Things (IoT), healthcare, manufacturing, agriculture, energy, remote sensing, and more [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “[92] Survey on FL-powered IoT applications to run on IoT networks X X X X X X”