# Adaptive Federated Learning With Gradient Compression in Uplink NOMA

arXiv: https://arxiv.org/abs/2003.01344 · HTML: https://mlsanity.com/paper/2003.01344/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Sparsified Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2112.12872/) (2021)
- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
- [User Scheduling for Federated Learning Through Over-the-Air Computation](https://mlsanity.com/paper/2108.02891/) (2021)
- [AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy](https://mlsanity.com/paper/2308.00258/) (2023)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [A-LAQ: Adaptive Lazily Aggregated Quantized Gradient](https://mlsanity.com/paper/2210.17474/) (2022)
- [Network Adaptive Federated Learning: Congestion and Lossy Compression](https://mlsanity.com/paper/2301.04430/) (2023)
- [OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning](https://mlsanity.com/paper/2105.00602/) (2021)
- [Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks](https://mlsanity.com/paper/2403.03157/) (2024)
- [Digital Over-the-Air Federated Learning in Multi-Antenna Systems](https://mlsanity.com/paper/2302.14648/) (2023)
- [Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization](https://mlsanity.com/paper/2405.17932/) (2024)
- [Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning](https://mlsanity.com/paper/2311.02130/) (2023)
- [FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication](https://mlsanity.com/paper/2312.00035/) (2023)
- [Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning](https://mlsanity.com/paper/2312.12417/) (2023)

## Abstract (arXiv)

Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices. Several unique features such as energy saving and privacy preserving make FL a highly promising learning approach for power-limited and privacy sensitive devices. Although distributed computing can lower down the information amount that needs to be uploaded, model updates in FL can still experience performance bottleneck, especially for updates via wireless connections. In this work, we investigate the performance of FL update with mobile edge devices that are connected to the parameter server (PS) with practical wireless links, where uplink update from user to PS has very limited capacity. Different from the existing works, we apply non-orthogonal multiple access (NOMA) together with gradient compression in the wireless uplink. Simulation results show that our proposed scheme can significantly reduce aggregation latency while achieving similar accuracy.