AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

2026-07-07 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

AirPASS is a novel system designed for over-the-air federated learning (AirFL) in wireless environments, specifically utilizing a multi-waveguide pinching antenna system (PASS) at the access point. The research focuses on optimizing AirFL by maximizing the number of selected devices while ensuring the aggregation distortion remains below a predefined threshold. This involves a complex, nonconvex joint optimization problem encompassing device selection, receive beamforming, and the precise placement of pinching antennas due to their intricate interdependencies. To tackle this, AirPASS employs an alternating optimization framework. This framework comprises two key methods: a homotopy-Riemannian margin-consolidation technique for device selection and receive beamforming under a static PASS configuration, and a homotopy-assisted geometry optimization method for dynamically adjusting pinching-antenna positions given fixed selected devices and beamformer. Experimental results demonstrate that AirPASS consistently surpasses conventional co-located MIMO baselines, achieves performance comparable to ideal FedAvg, and offers an attractive performance-complexity tradeoff against SDR-DC and matching-pursuit scheduling alternatives.

Key takeaway

For Machine Learning Engineers designing or deploying federated learning in wireless environments, AirPASS presents a robust solution to enhance system performance. You should consider integrating pinching antenna systems to maximize device selection while maintaining low aggregation distortion. This approach consistently outperforms traditional MIMO setups and offers a better performance-complexity tradeoff than SDR-DC, making it a strong candidate for your next-generation AirFL deployments.

Key insights

AirPASS optimizes over-the-air federated learning using pinching antennas, outperforming MIMO baselines and approaching ideal FedAvg.

Principles

• Joint optimization of AirFL variables is highly nonconvex.
• Alternating optimization can tackle complex coupled problems.
• Pinching antenna systems enhance AirFL performance.

Method

AirPASS uses alternating optimization: first, homotopy-Riemannian margin-consolidation for device selection/beamforming; then, homotopy-assisted geometry optimization for antenna placement.

In practice

• Implement AirPASS for improved AirFL efficiency.
• Consider pinching antennas for wireless FL deployments.
• Evaluate AirPASS against SDR-DC for scheduling.

Topics

• Over-the-Air Federated Learning
• Pinching Antenna Systems
• Alternating Optimization
• Device Selection
• Receive Beamforming
• Wireless Communication

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.