# Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks

arXiv: https://arxiv.org/abs/2501.11079 · HTML: https://mlsanity.com/paper/2501.11079/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 19 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Five Facets of 6G: Research Challenges and Opportunities](https://mlsanity.com/paper/2212.07902/) (2022)
- [Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation](https://mlsanity.com/paper/2303.16071/) (2023)
- [Simultaneously Transmitting and Reflecting Surface (STARS) for Terahertz Communications](https://mlsanity.com/paper/2212.00497/) (2022)
- [Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks](https://mlsanity.com/paper/2005.11544/) (2020)
- [D-STAR: Dual Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces for Joint Uplink/Downlink Transmission](https://mlsanity.com/paper/2307.16096/) (2023)
- [AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities](https://mlsanity.com/paper/2311.04241/) (2023)
- [Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI](https://mlsanity.com/paper/2304.13109/) (2023)
- [RIS-Assisted Energy-Efficient LEO Satellite Communications with NOMA](https://mlsanity.com/paper/2306.10422/) (2023)
- [Energy Minimization for IRS-aided WPCNs with Non-linear Energy Harvesting Model](https://mlsanity.com/paper/2108.13603/) (2021)

## Built on by (most cited first, 3 of 3)

- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks](https://mlsanity.com/paper/2601.00538/) (2026)
- [Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2604.14309/) (2026)

## Abstract (arXiv)

In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MF-RIS) in low-Earth orbit (LEO) is proposed. Unlike traditional RIS with only signal reflection capability, the MF-RIS can reflect, refract, and amplify signals, as well as harvest energy from wireless signals. Given the high energy demands in shadow regions where solar energy is unavailable, MF-RIS is deployed in LEO to enhance signal coverage and improve energy efficiency (EE). To address this, we formulate a long-term EE optimization problem by determining the optimal parameters for MF-RIS configurations, including amplification and phase-shifts, energy harvesting ratios, and LEO transmit beamforming. To address the complex non-convex and non-linear problem, a federated learning enhanced multi-agent deep deterministic policy gradient (FEMAD) scheme is designed. Multi-agent DDPG of each agent can provide the optimal action policy from its interaction to environments, whereas federated learning enables the hidden information exchange among multi-agents. In numerical results, we can observe significant EE improvements compared to the other benchmarks, including centralized deep reinforcement learning as well as distributed multi-agent deep deterministic policy gradient (DDPG). Additionally, the proposed LEO-MF-RIS architecture has demonstrated its effectiveness, achieving the highest EE performance compared to the scenarios of fixed/no energy harvesting in MF-RIS, traditional reflection-only RIS, and deployment without RISs/MF-RISs.