mlsanityData to 13 Oct 2026

Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review

arXiv 2308.04404 ↗First posted 1 Aug 2023cs.LG cs.NIcited by 0

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Abstract (arXiv)

These days with the rising computational capabilities of wireless user equipment such as smart phones, tablets, and vehicles, along with growing concerns about sharing private data, a novel machine learning model called federated learning (FL) has emerged. FL enables the separation of data acquisition and computation at the central unit, which is different from centralized learning that occurs in a data center. FL is typically used in a wireless edge network where communication resources are limited and unreliable. Bandwidth constraints necessitate scheduling only a subset of UEs for updates in each iteration, and because the wireless medium is shared, transmissions are susceptible to interference and are not assured. The article discusses the significance of Machine Learning in wireless communication and highlights Federated Learning (FL) as a novel approach that could play a vital role in future mobile networks, particularly 6G and beyond.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 44 of its 64 references that are in the map. First posted on arXiv 1 Aug 2023.

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44 of 64 references.

References · 44

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32 more
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