mlsanityData to 14 Oct 2026

Federated Learning in IoT: a Survey from a Resource-Constrained Perspective

arXiv 2308.13157 ↗First posted 25 Aug 2023cs.LG cs.DCcited by 2

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Abstract (arXiv)

The IoT ecosystem is able to leverage vast amounts of data for intelligent decision-making. Federated Learning (FL), a decentralized machine learning technique, is widely used to collect and train machine learning models from a variety of distributed data sources. Both IoT and FL systems can be complementary and used together. However, the resource-constrained nature of IoT devices prevents the widescale deployment FL in the real world. This research paper presents a comprehensive survey of the challenges and solutions associated with implementing Federated Learning (FL) in resource-constrained Internet of Things (IoT) environments, viewed from 2 levels, client and server. We focus on solutions regarding limited client resources, presence of heterogeneous client data, server capacity, and high communication costs, and assess their effectiveness in various scenarios. Furthermore, we categorize the solutions based on the location of their application, i.e., the IoT client, and the FL server. In addition to a comprehensive review of existing research and potential future directions, this paper also presents new evaluation metrics that would allow researchers to evaluate their solutions on resource-constrained IoT devices.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 55 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

35 of 55 references.

References · 35

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23 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learningcited by 0
  2. 2026Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentationcited by 0