Distribution-Aware Mobility-Assisted Decentralized Federated Learning
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
16 of 26 references.
References · 16
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 367
- 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
- 2019MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Samplingcited by 67
- 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
- 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 43
- 2022Topology-aware Generalization of Decentralized SGDcited by 38
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2024An Aggregation-Free Federated Learning for Tackling Data Heterogeneitycited by 33
- 2022Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Datacited by 32
- 2021Accelerating Gossip SGD with Periodic Global Averagingcited by 25
4 more
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
- 2021D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learningcited by 12
- 2022Data-heterogeneity-aware Mixing for Decentralized Learningcited by 12
- 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
Built on by
Citing papers per year: 2025: 1.
1 paper.