Tail-Latency-Aware Federated Learning with Pinching Antenna: Latency, Participation, and Placement

Area of Science:

• Wireless communication
• Machine learning
• Optimization theory

Background:

• Straggler synchronization is a key bottleneck in synchronous wireless federated learning (FL).
• Aggressively sampling only fast clients can hinder convergence on non-IID data due to statistical heterogeneity.
• Existing methods struggle to balance latency and statistical efficiency.

Purpose of the Study:

• To investigate a novel approach using a radiating pinching antenna (PASS) to reshape uplink latencies in FL.
• To jointly optimize PASS placement and client participation for improved time-to-accuracy.
• To analyze the impact of latency heterogeneity on convergence and participation strategies.

Main Methods:

• Developed a framework for PASS-enabled FL, optimizing antenna placement and client selection.
• Utilized order statistics to model expected maximum round latency and a heterogeneity-aware statistical-efficiency proxy.
• Derived first-order optimality conditions and analyzed latency gaps and sampling laws.

Main Results:

• Quantified a tail-latency premium amplified by maximum-order-statistic synchronization.
• Established a within-class square-root sampling law and a phase transition for slow-class participation.
• Demonstrated that PASS enables more eligible participation, leading to higher wall-clock accuracy.

Conclusions:

• PASS-enabled FL effectively mitigates straggler synchronization issues.
• The proposed optimization framework improves both latency and statistical efficiency.
• PASS offers a promising solution for faster and more accurate federated learning in heterogeneous wireless environments.