mlsanityData to 14 Oct 2026

Relay-Assisted Cooperative Federated Learning

arXiv 2107.09518 ↗First posted 20 Jul 2021cs.IT cs.LG cs.NIcited by 9

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Abstract (arXiv)

Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models by exploiting the superposition property of wireless multi-access channels. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted cooperative FL scheme to effectively address the straggler issue. In particular, we deploy multiple half-duplex relays to cooperatively assist the devices in uploading the local model updates to the edge server. The nature of the over-the-air computation poses system objectives and constraints that are distinct from those in traditional relay communication systems. Moreover, the strong coupling between the design variables renders the optimization of such a system challenging. To tackle the issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation with low complexity. Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large. The analysis provides critical insights on relay deployment in the implementation of cooperative FL. Extensive numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

18 of 33 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2021Convex optimizationcited by 2,515
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  6. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  7. 2018Federated Learning via Over-the-Air Computationcited by 219
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  9. 2019Federated Learning over Wireless Fading Channelscited by 183
  10. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
6 more
  1. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 96
  2. 2018Wireless Network Intelligence at the Edgecited by 92
  3. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33
  4. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 27
  5. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  6. 2019Optimized Power Control for Over-the-Air Computation in Fading Channelscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 0; 2025: 0; 2026: 1.

9 papers.

  1. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  2. 2023FedCiR: Client-Invariant Representation Learning for Federated Non-IID Featurescited by 12
  3. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  4. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
  5. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  6. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
  7. 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networkscited by 2
  8. 2023Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learningcited by 0
  9. 2026Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Over-the-Air Federated Learning via Second-Order Optimization
  2. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  3. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks
  4. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  5. 2025Biased Federated Learning under Wireless Heterogeneity
  6. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  7. 2025Optimal Transceiver Design in Over-the-Air Federated Distillation
  8. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control