A Survey on Federated Learning for Resource-Constrained IoT Devices

By Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, M. Hadi Amini. IEEE Internet of Things Journal, 2021

694 citations recorded by the source corpus.

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoT)-enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey article, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/195b88ba2ab7bc56ed24ea5b5d3f28622d2c6166

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Citations in the corpus

Decentralized Federated Averaging (2021, methodology): “The federated learningmethod has been applied to Internet of Things (IoT) researches [13].”
Asynchronous Federated Learning on Heterogeneous Devices: A Survey (2021, background): “[17] FL on IoT Only 7 papers related to AFL are gathered with an investigation on the convergence.”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “3) Computational and storage resource on the server side are also challenged [92], [93], [94].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “Compared with these works [10], [19], [20], [21], [22], [23], the main focus of this paper is to provide a comprehensive overview of state-of-the-art signal processing techniques for EL over B5G networks.”