Literature Database Entry

wu2026hierarchical

Mengfan Wu, Mate Boban and Falko Dressler, "Hierarchical Federated Learning in Device-to-Device Networks with Learning-Topology Co-Optimization," IEEE Transactions on Mobile Computing, March 2026. (online first)

Abstract

Federated learning (FL) enables collaborative model training across distributed devices while preserving privacy. However, growing heterogeneity in device resources and communication links challenges conventional FL, especially when relying on a single central server. Hierarchical federated learning (HFL) mitigates these issues by organizing devices into clusters coordinated through intermediate aggregators. Yet, the effectiveness of HFL critically depends on how clusters are formed: intra-cluster communication must be efficient, device computational capacities should be balanced to reduce stragglers, and data heterogeneity must be managed to ensure stable convergence. In this work, we propose a learning–topology co-optimization framework for HFL in networks where nodes communicate with each other with links of varying quality (e.g., device-to-device (D2D) or mesh networks). Our method jointly optimizes device connection topology and learning directions, leading to communication-efficient clusters that remain well aligned in optimization space. We provide a convergence analysis under mild assumptions, showing how inter- and intra-cluster divergence affect learning stability. Extensive experiments demonstrate that our approach consistently improves HFL performance, yielding at least a 6% accuracy gain under unbalanced data distributions and over 16% reduction in training time for regression tasks compared with existing clustering algorithms.

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Mengfan Wu
Mate Boban
Falko Dressler

BibTeX reference

@article{wu2026hierarchical,
    author = {Wu, Mengfan and Boban, Mate and Dressler, Falko},
    doi = {10.1109/TMC.2026.3673393},
    note = {to appear},
    title = {{Hierarchical Federated Learning in Device-to-Device Networks with Learning-Topology Co-Optimization}},
    journal = {IEEE Transactions on Mobile Computing},
    issn = {1536-1233},
    publisher = {IEEE},
    month = {3},
    year = {2026},
   }
   
   

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Last modified: 2026-04-29