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

arXiv: https://arxiv.org/abs/2607.19759 · HTML: https://mlsanity.com/paper/2607.19759/

## 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 (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020)
- [Device Scheduling with Fast Convergence for Wireless Federated Learning](https://mlsanity.com/paper/1911.00856/) (2019)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Low-Latency Federated Learning over Wireless Channels with Differential Privacy](https://mlsanity.com/paper/2106.13039/) (2021)
- [Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective](https://mlsanity.com/paper/2003.00866/) (2020)
- [Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks](https://mlsanity.com/paper/2210.14700/) (2022)
- [Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning](https://mlsanity.com/paper/2307.09263/) (2023)
- [Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity](https://mlsanity.com/paper/2308.03521/) (2023)
- [Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design](https://mlsanity.com/paper/2407.13703/) (2024)
- [Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning](https://mlsanity.com/paper/2206.09578/) (2022)
- [Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning](https://mlsanity.com/paper/2404.08324/) (2024)
- [Hierarchical Federated Learning for Social Network with Mobility](https://mlsanity.com/paper/2509.14938/) (2025)
- [RIS-Aided Spatial Scattering Modulation for mmWave MIMO Transmissions](https://mlsanity.com/paper/2307.14662/) (2023)
- [On the Performance of RIS-Aided Spatial Modulation for Downlink Transmission](https://mlsanity.com/paper/2402.02893/) (2024)
- [Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization](https://mlsanity.com/paper/2402.12957/) (2024)

## 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.