mlsanityData to 14 Oct 2026

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

arXiv 2412.05823 ↗First posted 8 Dec 2024cs.LG cs.AIcited by 9

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Abstract (arXiv)

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a realworld FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://github.com/jyzgh/DapperFL.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 40 of its 57 references that are in the map. First posted on arXiv 8 Dec 2024.

Builds on

40 of 57 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 6.

9 papers.

  1. 2025Toward a Holistic Approach to Continual Model Mergingcited by 3
  2. 2026TAP-ViTs: Task-Adaptive Pruning for On-Device Deployment of Vision Transformerscited by 1
  3. 2026CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstructioncited by 1
  4. 2024Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shiftcited by 0
  5. 2025Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devicescited by 0
  6. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  7. 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptationcited by 0
  8. 2026Autonomous Drift Learning in Data Streams: A Unified Perspectivecited by 0
  9. 2026FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devicescited by 0