# Federated Learning via Intelligent Reflecting Surface

arXiv: https://arxiv.org/abs/2011.05051 · HTML: https://mlsanity.com/paper/2011.05051/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 30 of 38)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning](https://mlsanity.com/paper/2201.09531/) (2022)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Over-the-Air Federated Learning via Second-Order Optimization](https://mlsanity.com/paper/2203.15488/) (2022)
- [Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point](https://mlsanity.com/paper/2203.13950/) (2022)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference](https://mlsanity.com/paper/2305.05933/) (2023)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations](https://mlsanity.com/paper/2210.02235/) (2022)
- [Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air](https://mlsanity.com/paper/2409.06343/) (2024)
- [Over-the-Air Federated Learning via Weighted Aggregation](https://mlsanity.com/paper/2409.07822/) (2024)
- [Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control](https://mlsanity.com/paper/2210.01452/) (2022)
- [Integrated Sensing-Communication-Computation for Edge Artificial Intelligence](https://mlsanity.com/paper/2306.01162/) (2023)
- [CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2102.10749/) (2021)
- [Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing](https://mlsanity.com/paper/2512.03719/) (2025)
- [Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2109.02353/) (2021)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks](https://mlsanity.com/paper/2308.08792/) (2023)
- [Over-the-Air Federated Learning with Retransmissions (Extended Version)](https://mlsanity.com/paper/2111.10267/) (2021)
- [Matching Pursuit Based Scheduling for Over-the-Air Federated Learning](https://mlsanity.com/paper/2206.06679/) (2022)
- [Digital Over-the-Air Federated Learning in Multi-Antenna Systems](https://mlsanity.com/paper/2302.14648/) (2023)

## Abstract (arXiv)

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex-functions programming algorithm for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results will demonstrate that our proposed algorithm and the deployment of an IRS can achieve a lower training loss and higher FL prediction accuracy than the baseline algorithms.