Revolutionizing Wireless Networks with Federated Learning: A
  Comprehensive Review
Sajjad Emdadi Mahdimahalleh
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.