# Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning

arXiv: https://arxiv.org/abs/2307.09263 · HTML: https://mlsanity.com/paper/2307.09263/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 18 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)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks](https://mlsanity.com/paper/2210.14700/) (2022)
- [Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling](https://mlsanity.com/paper/2105.14708/) (2021)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling](https://mlsanity.com/paper/2106.06934/) (2021)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Federated Learning for Social Network with Mobility](https://mlsanity.com/paper/2509.14938/) (2025)
- [Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization](https://mlsanity.com/paper/2402.12957/) (2024)
- [Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning](https://mlsanity.com/paper/2607.19759/) (2026)

## Abstract (arXiv)

As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance.