mlsanityData to 14 Oct 2026

Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

arXiv 2607.19759 ↗First posted 22 Jul 2026cs.LG cs.AI cs.ITcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communication reliability, existing wireless FL studies rarely characterize the trade-off between learning convergence and communication delay under modulation-dependent transmission errors. In this paper, we consider a wireless FL system operating under RIS-assisted blocked-link propagation scenarios, and focus on adaptive modulation and sub-channel allocation for convergence-latency aware communication design. By characterizing the effect of symbol errors on uploaded local gradients, we derive a convergence-related upper bound that reveals the impact of symbol error rate (SER) on FL loss decay. Based on this result, we formulate a joint convergence-latency optimization problem, which is cast as a mixed-integer nonlinear programming (MINLP) problem, and solve it using a low-complexity hybrid alternating optimization framework. Extensive experiments on MNIST, CIFAR-10, and Speech Commands show that the proposed scheme consistently achieves faster convergence and higher test accuracy than existing adaptive communication schemes, especially in complex tasks and challenging wireless scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 22 Jul 2026.

Builds on

18 of 33 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  3. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  4. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  5. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  6. 2019Wireless Communications Through Reconfigurable Intelligent Surfacescited by 37
  7. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
  8. 2020Enabling AI in Future Wireless Networks: A Data Life Cycle Perspectivecited by 14
  9. 2022Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networkscited by 14
  10. 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learningcited by 3
  11. 2023Analysis and Optimization of Wireless Federated Learning with Data Heterogeneitycited by 3
  12. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
6 more
  1. 2022Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learningcited by 2
  2. 2024Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learningcited by 2
  3. 2025Hierarchical Federated Learning for Social Network with Mobilitycited by 2
  4. 2023RIS-Aided Spatial Scattering Modulation for mmWave MIMO Transmissionscited by 1
  5. 2024On the Performance of RIS-Aided Spatial Modulation for Downlink Transmissioncited by 1
  6. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantizationcited by 1

Built on by

Not cited yet.