FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
<|reference_start|>FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning: Despite extensive research into data heterogeneity in federated learning (FL), system heterogeneity remains a significant yet often overlooked challenge. Traditional FL approaches typically assume homogeneous hardware resources across FL clients, implying that clients can train a global model within a comparable time frame. However, in practical FL systems, clients often have heterogeneous resources, which impacts their training capacity. This discrepancy underscores the importance of exploring model-heterogeneous FL, a paradigm allowing clients to train different models based on their resource capabilities. To address this challenge, we introduce FedTSA, a cluster-based two-stage aggregation method tailored for system heterogeneity in FL. FedTSA begins by clustering clients based on their capabilities, then performs a two-stage aggregation: conventional weight averaging for homogeneous models in Stage 1, and deep mutual learning with a diffusion model for aggregating heterogeneous models in Stage 2. Extensive experiments demonstrate that FedTSA not only outperforms the baselines but also explores various factors influencing model performance, validating FedTSA as a promising approach for model-heterogeneous FL.<|reference_end|>
@article{fan2024fedtsa:,
    title={FedTSA: A Cluster-based Two-Stage Aggregation Method for
  Model-heterogeneous Federated Learning},
    author={Boyu Fan, Chenrui Wu, Xiang Su and Pan Hui},
    journal={arXiv preprint arXiv:2407.05098},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05098},
    primaryClass={cs.LG cs.AI}
}