# Federated Learning via Active RIS Assisted Over-the-Air Computation

arXiv: https://arxiv.org/abs/2311.03982 · HTML: https://mlsanity.com/paper/2311.03982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation](https://mlsanity.com/paper/2602.02469/) (2026)

## Abstract (arXiv)

In this paper, we propose leveraging the active reconfigurable intelligence surface (RIS) to support reliable gradient aggregation for over-the-air computation (AirComp) enabled federated learning (FL) systems. An analysis of the FL convergence property reveals that minimizing gradient aggregation errors in each training round is crucial for narrowing the convergence gap. As such, we formulate an optimization problem, aiming to minimize these errors by jointly optimizing the transceiver design and RIS configuration. To handle the formulated highly non-convex problem, we devise a two-layer alternative optimization framework to decompose it into several convex subproblems, each solvable optimally. Simulation results demonstrate the superiority of the active RIS in reducing gradient aggregation errors compared to its passive counterpart.