arXiv:2607.06768v1 Announce Type: cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS). We adopt the widely studied learning-oriented AirFL formulation, which seeks to maximize the number of selected devices while keeping the aggregation distortion below a prescribed threshold. The resulting joint optimization of device selection, receive beamforming, and pinching-antenna placement is highly nonconvex due to the intricate coupling among these system variables.
The rapid expansion of AI and distributed learning models necessitates more efficient and optimized wireless communication methods to support their deployment and scalability.
This development is crucial for enabling robust and efficient on-device AI and federated learning, reducing computational and transmission overheads, and improving data privacy in distributed AI systems.
The proposed 'pinching antenna system' for over-the-air federated learning changes how wireless devices can collectively train AI models, potentially making such systems more performant and scalable.
- · AI hardware manufacturers
- · Telecommunications companies
- · Edge AI developers
- · Distributed computing platforms
- · Inefficient wireless communication protocols
- · Data centers relying solely on wired connectivity for distributed training
More efficient and faster federated learning deployments enabled by improved wireless resource allocation.
Accelerated development and adoption of AI applications requiring real-time, on-device intelligence without centralizing raw data.
Enhanced data privacy and security for AI models due to local processing and only aggregated parameters being shared over the air.
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Read at arXiv cs.LG