mlsanityData to 7 Oct 2026

Capture Global Feature Statistics for One-Shot Federated Learning

arXiv 2503.06962 ↗First posted 10 Mar 2025cs.LG cs.AIcited by 6

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Abstract (arXiv)

Traditional Federated Learning (FL) necessitates numerous rounds of communication between the server and clients, posing significant challenges including high communication costs, connection drop risks and susceptibility to privacy attacks. One-shot FL has become a compelling learning paradigm to overcome above drawbacks by enabling the training of a global server model via a single communication round. However, existing one-shot FL methods suffer from expensive computation cost on the server or clients and cannot deal with non-IID (Independent and Identically Distributed) data stably and effectively. To address these challenges, this paper proposes FedCGS, a novel Federated learning algorithm that Capture Global feature Statistics leveraging pre-trained models. With global feature statistics, we achieve training-free and heterogeneity-resistant one-shot FL. Furthermore, we extend its application to personalization scenario, where clients only need execute one extra communication round with server to download global statistics. Extensive experimental results demonstrate the effectiveness of our methods across diverse data heterogeneity settings. Code is available at https://github.com/Yuqin-G/FedCGS.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

32 of 50 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statisticscited by 2
  2. 2025Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregationcited by 1
  3. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  4. 2025Single-Round Scalable Analytic Federated Learningcited by 0
  5. 2026FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuningcited by 0
  6. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0