One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
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Abstract (arXiv)
Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel (MAC) from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital quadrature amplitude modulation (QAM) at edge devices and over-the-air majority-voting based decoding at edge server. We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.
Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2020.
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Citing papers per year: 2021: 30; 2022: 26; 2023: 20; 2024: 12; 2025: 13; 2026: 7.
108 papers.
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- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
- 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
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- 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
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50 most cited of 108 All 108 (JSON)
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- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks31 Jul 2026
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning8 May 2026
- 2026Semantic-based Distributed Learning for Diverse and Discriminative Representations20 Apr 2026
- 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition5 Apr 2026
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers30 Dec 2025
- 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning22 Dec 2025
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- 2025Closing the Generalization Gap in Parameter-efficient Federated Edge Learning28 Nov 2025
- 2025Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching17 Nov 2025
38 more
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- 2025Learned Digital Over-the-Air Computing for Federated Edge Learning20 Sep 2025
- 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Up25 Aug 2025
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control2 Apr 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks23 Dec 2024
- 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagation19 Dec 2024
- 2024Over-the-Air Federated Learning via Weighted Aggregation12 Sep 2024
- 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air10 Sep 2024
- 2024Scalable and Reliable Over-the-Air Federated Edge Learning16 Jul 2024
- 2024Waveform Design for Over-the-Air Computing31 May 2024
- 2024A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning20 Apr 2024
- 2024Biased Over-the-Air Federated Learning under Wireless Heterogeneity28 Mar 2024
- 2024Training Machine Learning models at the Edge: A Survey5 Mar 2024
- 2024Federated Learning via Lattice Joint Source-Channel Coding1 Mar 2024
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligence26 Feb 2024
- 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces22 Jan 2024
- 2023Sparse Training for Federated Learning with Regularized Error Correction21 Dec 2023
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
- 2023Blind Federated Learning via Over-the-Air q-QAM7 Nov 2023
- 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning27 Oct 2023
- 2023Over-the-air Federated Policy Gradient25 Oct 2023
- 2023Accelerating Split Federated Learning over Wireless Communication Networks24 Oct 2023
- 2023Over-the-Air Federated Learning and Optimization16 Oct 2023
- 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework4 Oct 2023
- 2023Federated Linear Bandit Learning via Over-the-Air Computation25 Aug 2023
- 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient17 Aug 2023
- 2023Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computation11 Aug 2023
- 2023Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation5 Aug 2023
- 2023Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information7 Jul 2023
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions3 Jul 2023
- 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning5 Jun 2023
- 2023Distributed Learning over Networks with Graph-Attention-Based Personalization22 May 2023
- 2023Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping18 May 2023
- 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgets5 May 2023
50 newest of 108 All 108 (JSON)
- 2021Federated Learning for Smart Healthcare: A Surveycited by 182
- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
- 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 31
- 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
- 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
12 most cited of 108 All 108 (JSON)
- 2026AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learning19 Sep 2026
- 2026Communication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching4 Sep 2026
- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks31 Jul 2026
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning8 May 2026
- 2026Semantic-based Distributed Learning for Diverse and Discriminative Representations20 Apr 2026
- 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition5 Apr 2026
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers30 Dec 2025
- 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning22 Dec 2025
- 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing3 Dec 2025
- 2025Closing the Generalization Gap in Parameter-efficient Federated Edge Learning28 Nov 2025
- 2025Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching17 Nov 2025
12 newest of 108 All 108 (JSON)