# Federated Learning over a Wireless Network: Distributed User Selection through Random Access

arXiv: https://arxiv.org/abs/2307.03758 · HTML: https://mlsanity.com/paper/2307.03758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 7 Jul 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)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [On the distance between two neural networks and the stability of learning](https://mlsanity.com/paper/2002.03432/) (2020)
- [Federated Transfer Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1910.06001/) (2019)
- [Multi-objective Evolutionary Federated Learning](https://mlsanity.com/paper/1812.07478/) (2018)
- [Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges](https://mlsanity.com/paper/2001.08159/) (2020)
- [V2X in 3GPP Standardization: NR Sidelink in Rel-16 and Beyond](https://mlsanity.com/paper/2104.11135/) (2021)
- [FedFly: Towards Migration in Edge-based Distributed Federated Learning](https://mlsanity.com/paper/2111.01516/) (2021)

## Built on by (most cited first, 2 of 2)

- [On the Combination of AI and Wireless Technologies: 3GPP Standardization Progress](https://mlsanity.com/paper/2407.10984/) (2024)
- [DiReDi: Distillation and Reverse Distillation for AIoT Applications](https://mlsanity.com/paper/2409.08308/) (2024)

## Abstract (arXiv)

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach.