# Distribution-Aware Mobility-Assisted Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2505.18866 · HTML: https://mlsanity.com/paper/2505.18866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling](https://mlsanity.com/paper/1905.09435/) (2019)
- [FedDisco: Federated Learning with Discrepancy-Aware Collaboration](https://mlsanity.com/paper/2305.19229/) (2023)
- [Decentralized Federated Learning via Mutual Knowledge Transfer](https://mlsanity.com/paper/2012.13063/) (2020)
- [Topology-aware Generalization of Decentralized SGD](https://mlsanity.com/paper/2206.12680/) (2022)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [An Aggregation-Free Federated Learning for Tackling Data Heterogeneity](https://mlsanity.com/paper/2404.18962/) (2024)
- [Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data](https://mlsanity.com/paper/2204.04452/) (2022)
- [Accelerating Gossip SGD with Periodic Global Averaging](https://mlsanity.com/paper/2105.09080/) (2021)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning](https://mlsanity.com/paper/2104.07365/) (2021)
- [Data-heterogeneity-aware Mixing for Decentralized Learning](https://mlsanity.com/paper/2204.06477/) (2022)
- [Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying](https://mlsanity.com/paper/2206.04742/) (2022)

## Built on by (most cited first, 1 of 1)

- [Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach](https://mlsanity.com/paper/2512.24694/) (2025)

## Abstract (arXiv)

Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients can be mobile, yet the impact of user mobility on DFL performance remains largely unexplored, despite its potential to facilitate communication and model convergence. In this work, we demonstrate that introducing a small fraction of mobile clients, even with random movement, can significantly improve the accuracy of DFL by facilitating information flow. To further enhance performance, we propose novel distribution-aware mobility patterns, where mobile clients strategically navigate the network, leveraging knowledge of data distributions and static client locations. The proposed moving strategies mitigate the impact of data heterogeneity and boost learning convergence. Extensive experiments validate the effectiveness of induced mobility in DFL and demonstrate the superiority of our proposed mobility patterns over random movement.