# Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces

arXiv: https://arxiv.org/abs/2401.12149 · HTML: https://mlsanity.com/paper/2401.12149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Federated Learning via Intelligent Reflecting Surface](https://mlsanity.com/paper/2011.05051/) (2020)
- [Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://mlsanity.com/paper/2011.10282/) (2020)
- [Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement](https://mlsanity.com/paper/1911.05326/) (2019)
- [Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control](https://mlsanity.com/paper/2205.05867/) (2022)
- [Federated Learning in Multi-RIS Aided Systems](https://mlsanity.com/paper/2010.13333/) (2020)
- [CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks](https://mlsanity.com/paper/2205.09330/) (2022)
- [Personalizing Federated Learning with Over-the-Air Computations](https://mlsanity.com/paper/2302.12509/) (2023)
- [ROAR-Fed: RIS-Assisted Over-the-Air Adaptive Resource Allocation for Federated Learning](https://mlsanity.com/paper/2309.09883/) (2023)
- [RIS-Assisted Over-the-Air Adaptive Federated Learning with Noisy Downlink](https://mlsanity.com/paper/2309.10758/) (2023)
- [Iterative Power Control for Wireless Networks with Distributed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2212.01321/) (2022)

## Abstract (arXiv)

Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i.d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.