mlsanityData to 7 Oct 2026

FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection

arXiv 2410.11397 ↗First posted 15 Oct 2024cs.LGcited by 14

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Abstract (arXiv)

Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data and unexpected out-of-distribution (OOD) data, such as covariate-shift and semantic-shift data. Current FL researches typically address either covariate-shift data through OOD generalization or semantic-shift data via OOD detection, overlooking the simultaneous occurrence of various OOD shifts. In this work, we propose FOOGD, a method that estimates the probability density of each client and obtains reliable global distribution as guidance for the subsequent FL process. Firstly, SM3D in FOOGD estimates score model for arbitrary distributions without prior constraints, and detects semantic-shift data powerfully. Then SAG in FOOGD provides invariant yet diverse knowledge for both local covariate-shift generalization and client performance generalization. In empirical validations, FOOGD significantly enjoys three main advantages: (1) reliably estimating non-normalized decentralized distributions, (2) detecting semantic shift data via score values, and (3) generalizing to covariate-shift data by regularizing feature extractor. The prejoct is open in https://github.com/XeniaLLL/FOOGD-main.git.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 67 of its 90 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

67 of 90 references.

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Citing papers per year: 2025: 10; 2026: 4.

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  1. 2025When Shift Happens - Confounding Is to Blamecited by 5
  2. 2025Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalizationcited by 4
  3. 2025Global Intervention and Distillation for Federated Out-of-Distribution Generalizationcited by 3
  4. 2025FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Modelscited by 2
  5. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  6. 2026Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Datacited by 1
  7. 2025DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imagingcited by 0
  8. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  9. 2025ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classificationcited by 0
  10. 2025TreeFedDG: Alleviating Global Drift in Federated Domain Generalization for Medical Image Segmentationcited by 0
  11. 2025FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OODcited by 0
  12. 2026Autonomous Drift Learning in Data Streams: A Unified Perspectivecited by 0
2 more
  1. 2026Silent Failures in Federated Personalization of Foundation Modelscited by 0
  2. 2026Out-of-Distribution Federated Distillation with Domain-Aware Proxycited by 0