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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Resource constrained learning over wireless networks.

H Vincent Poor1

  • 1Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|February 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Next-generation wireless networks will integrate artificial intelligence (AI) at the edge. This paper examines wireless federated learning, optimizing AI for resource-limited networks by balancing energy, bandwidth, and privacy.

Keywords:
machine learningresource constraintswireless networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Next-generation wireless networks are increasingly integrating artificial intelligence (AI) across all layers.
  • A significant trend is migrating AI and machine learning (ML) functions to the network edge, driven by edge-device applications, data locality, and fog/edge computing advancements.
  • Wireless federated learning (WFL) enables collaborative model building on edge devices using local data via an aggregator.

Purpose of the Study:

  • To explore the integration of AI and machine learning within wireless networks, specifically focusing on edge computing paradigms.
  • To investigate the challenges and trade-offs inherent in wireless federated learning (WFL) due to the resource-constrained nature of wireless links.
  • To analyze the interplay between wireless communication characteristics and ML algorithm performance in edge AI applications.

Main Methods:

  • Exploration of wireless federated learning (WFL) as a framework for edge AI.
  • Analysis of trade-offs between energy consumption, bandwidth efficiency, learning rate, and data privacy in WFL.
  • Consideration of the wireless medium's impact on the design and implementation of AI applications at the network edge.

Main Results:

  • The study highlights the necessity of considering wireless medium interactions in AI/ML design for edge applications.
  • Identified key trade-offs exist between energy efficiency, bandwidth usage, learning speed, and data privacy in WFL systems.
  • The research provides insights into optimizing AI deployment in resource-limited wireless edge environments.

Conclusions:

  • Effective AI integration in future wireless networks requires a holistic approach, considering both network and ML aspects.
  • Optimizing wireless federated learning involves carefully managing the trade-offs between performance metrics and resource constraints.
  • This work contributes to the development of sustainable AI within the context of wireless edge computing.