# A Practical Cross-Device Federated Learning Framework over 5G Networks

arXiv: https://arxiv.org/abs/2204.08134 · HTML: https://mlsanity.com/paper/2204.08134/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [CrypTFlow2: Practical 2-Party Secure Inference](https://mlsanity.com/paper/2010.06457/) (2020)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels](https://mlsanity.com/paper/2307.08809/) (2023)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance](https://mlsanity.com/paper/2310.02698/) (2023)
- [Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization](https://mlsanity.com/paper/2412.02535/) (2024)
- [Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis](https://mlsanity.com/paper/2609.15681/) (2026)
- [Lightweight and Robust Federated Data Valuation](https://mlsanity.com/paper/2509.25560/) (2025)

## Abstract (arXiv)

The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising the privacy. However, limited by the capacity of wireless data transmission, the employment of federated learning on mobile devices has been making slow progress in practical. The development and commercialization of the 5th generation (5G) mobile networks has shed some light on this. In this paper, we analyze the challenges of existing federated learning schemes for mobile devices and propose a novel cross-device federated learning framework, which utilizes the anonymous communication technology and ring signature to protect the privacy of participants while reducing the computation overhead of mobile devices participating in FL. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL. We also give a case study of autonomous driving. Finally, we present the performance evaluation of the proposed scheme and discuss some open issues in federated learning.