# Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks

arXiv: https://arxiv.org/abs/2011.12469 · HTML: https://mlsanity.com/paper/2011.12469/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks](https://mlsanity.com/paper/2311.16538/) (2023)
- [Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2305.07845/) (2023)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization](https://mlsanity.com/paper/2305.13503/) (2023)
- [Fair Concurrent Training of Multiple Models in Federated Learning](https://mlsanity.com/paper/2404.13841/) (2024)
- [Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets](https://mlsanity.com/paper/2208.04322/) (2022)
- [Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks](https://mlsanity.com/paper/2305.02109/) (2023)
- [Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services](https://mlsanity.com/paper/2508.16037/) (2025)

## Abstract (arXiv)

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL, regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the MS-FEDL problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.