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

To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer conditions such as accurate channel state information (CSI), tight time/frequency synchronization, and frequent transceiver calibration for signal alignment. However, these requirements, if not impossible to be met, incur substantial communication and computation overhead. In this paper, we propose a non-coherent AirFL (NCAirFL) protocol over a broadband single-antenna MAC, leveraging binary dithering, unbiased non-coherent detection, and long-term error feedback to waive the need for instantaneous CSI. For NCAirFL with general smooth non-convex objectives and a constant learning rate, we establish a convergence bound achieving the convergence rate in the same order of $\mathcal{O}(1/\sqrt{T})$ as communication-ideal FedAvg, where $T$ is the total number of communication rounds. To further improve communication efficiency under data and wireless resource heterogeneity, we also derive a lower bound on the expected single-round objective decrease in the global loss conditioned on device scheduling, building upon which a surrogate objective function is obtained for jointly optimal device selection and power control. Experimental results on MNIST and CIFAR-10 corroborate that NCAirFL achieves learning performance close to FedAvg in practical settings, with the proposed device scheduling policy substantially accelerating convergence.

Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the network edge. By exploiting wireless superposition over a shared multiple-access channel, AirFL turns simultaneous transmissions of local model updates into an analog aggregate at the receiver, thereby reducing communication latency, bandwidth usage, and energy consumption in wireless aggregation domains. This article develops a design-oriented tutorial view of analog AirFL. We organize existing schemes according to the signal-processing mechanism used to enable AirFL aggregation: transmitter-side channel compensation and power control, receiver-side equalization and high-dimensional processing, or learning-aware aggregation weighting. This viewpoint leads to three representative classes -- CSIT-aware, blind, and weighted AirFL -- and clarifies their assumptions, performance tradeoffs, complexity, and deployment limitations. We further discuss synchronization, digital and hybrid analog-digital realization, and open research directions for integrating AirFL into practical wireless edge-AI systems.

Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmission slot, significantly reducing communication latency. While OTA computation has been extensively studied for centralized federated learning (FL), its integration with decentralized federated learning (DFL) remains largely unexplored, and principled communication topology selection is absent from existing work. We present AIRPLAN, a query-optimized topology selection framework for Over-the-Air Decentralized Federated Learning (OTA-DFL). AIRPLAN establishes a formal equivalence between OTA-DFL and distributed query processing, enabling topology selection to be formulated as a cost-based query optimization problem. Using privacy-preserving Count-Min Sketch statistics, AIRPLAN estimates workload characteristics, evaluates a graph-aware cost model across candidate topologies, and selects the communication graph that minimizes training cost while satisfying a target accuracy SLA. Experiments across five graph families, three vision benchmarks, four client scales, and multiple SNR settings show that AIRPLAN matches the oracle-optimal topology in 91.4% of workloads while introducing less than 1.8% overhead. We further derive theoretical error bounds for topology-aware sparsification, demonstrating that well-connected topologies better tolerate aggressive compression. AIRPLAN introduces a systems-oriented perspective that bridges wireless federated learning and distributed query optimization.