FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning

• URL: http://arxiv.org/abs/2407.05098v2
• Date: Mon, 15 Jul 2024 08:19:30 GMT
• Title: FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
• Authors: Boyu Fan, Chenrui Wu, Xiang Su, Pan Hui,
• Abstract summary: FedTSA is a cluster-based two-stage aggregation method tailored for system heterogeneity in federated learning (FL) We show that FedTSA outperforms the baselines and explores various factors influencing model performance.
• Score: 10.254813698006103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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