Over-the-Air Federated Learning

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Overview

Over-the-air federated learning studies how wireless devices can train a shared or personalized model by transmitting updates whose signals combine over the air, reducing communication time. Methods include channel-aware precoding and scheduling, compression and error feedback, and noncoherent schemes that aggregate signal energies without precise phase or channel estimates. Convergence analyses and evaluations on vision benchmarks such as MNIST and CIFAR-10 show that learning can approach communication-ideal federated averaging under stated assumptions, including in heterogeneous wireless settings. A central tension is how to balance estimation and synchronization overhead against aggregation noise, device availability, energy limits, and robustness to changing channels and uneven data.

Latest papers

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11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

  1. Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks

    Sep 26, 2026Ming Xiang, Nicolò Michelusi, Yonina C. Eldar +1Over-the-Air Federated LearningFederated Learning Aggregation

    Northeastern University, Boston, MA, USA · Arizona State University, Tempe, AZ, USA · Weizmann Institute of Science, Rehovot, Israel

  2. AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learning

    Sep 19, 2026Jonggyu Jang, Hyeonsu Lyu, Hyun Jong YangOver-the-Air Federated LearningFederated Learning Aggregation

    Department of Electronics Engineering, Chung-nam National University, Daejeon 34134, Republic of Korea · Institute of New Media and Communications, Seoul 08826, Republic of Korea · Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea

  3. Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling

    Sep 8, 2026Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +2Federated Learning AggregationCommunication-Efficient Distributed Training

    IoT Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA · Institute for Intelligent Networked Systems, Northeastern University London, London, E1 8PH, UK +2

  4. Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

    Aug 2, 2026Furkan Bagci, Busra Tegin, Mohammad Kazemi +1Federated Learning AggregationNon-IID Federated Learning

    Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA · IETR-UMR CNRS 6164, Centrale-Supélec Rennes Campus, Cesson-Sévigné 35576, France · Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, U.K. +1

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

    Jul 7, 2026Seyed Mohammad Azimi-Abarghouyi, Christopher G. BrintonFederated Learning AggregationWireless Communications

    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden · School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN USA

  6. Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning

    Jul 5, 2026Kaushal Attaluri, Rebeca P. Diaz-Redondo, Manuel Fernandez VeigaCommunication Topology OptimizationFederated Learning Aggregation

    ICLab, atlanTTic, University of Vigo

  7. Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

    Jul 5, 2026Zubaida Fatima, Zubair Shaban, Yusuf Jamal +3Federated Learning AggregationOver-the-Air Federated Learning

    IIIT, Delhi · IIT Dharwad

  8. Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels

    May 28, 2026Anthony Ayli, Khalil Harris, Jihad Fahs +1Federated Learning AggregationHomomorphic Encryption

  9. Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

    May 8, 2026Hao Chen, Zavareh BozorgaslFederated Learning AggregationWireless Communications

    Signal, Communication, and Learning Lab (SCALE Lab), Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA

  10. SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation

    Feb 17, 2026Hao Chen, Zavareh BozorgaslFederated Learning AggregationOver-the-Air Federated Learning

    Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA.

  11. Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing

    Dec 3, 2025Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione, Kaibin HuangFederated Learning AggregationCommunication-Efficient Distributed Training

    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China