# Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations

arXiv: https://arxiv.org/abs/2306.01306 · HTML: https://mlsanity.com/paper/2306.01306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Invariant Risk Minimization Games](https://mlsanity.com/paper/2002.04692/) (2020)
- [Distributionally Robust Federated Averaging](https://mlsanity.com/paper/2102.12660/) (2021)
- [FL Games: A Federated Learning Framework for Distribution Shifts](https://mlsanity.com/paper/2211.00184/) (2022)
- [Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.04723/) (2020)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2010.04376/) (2020)
- [Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2009.13988/) (2020)
- [Federated Distributionally Robust Optimization for Phase Configuration of RISs](https://mlsanity.com/paper/2108.09026/) (2021)
- [Deep-Learning-Assisted Configuration of Reconfigurable Intelligent Surfaces in Dynamic rich-scattering Environments](https://mlsanity.com/paper/2202.07884/) (2022)
- [Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations](https://mlsanity.com/paper/2105.01771/) (2021)

## Abstract (arXiv)

In this paper, we investigate the problem of robust Reconfigurable Intelligent Surface (RIS) phase-shifts configuration over heterogeneous communication environments. The problem is formulated as a distributed learning problem over different environments in a Federated Learning (FL) setting. Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments. Using Invariant Risk Minimization (IRM) and its FL equivalent, dubbed FL Games, we solve the RIS configuration problem by learning invariant causal representations across multiple environments and then predicting the phases. The solution corresponds to playing according to Best Response Dynamics (BRD) which yields the Nash Equilibrium of the FL game. The representation learner and the phase predictor are modeled by two neural networks, and their performance is validated via simulations against other benchmarks from the literature. Our results show that causality-based learning yields a predictor that is 15% more accurate in unseen Out-of-Distribution (OoD) environments.