# Global Intervention and Distillation for Federated Out-of-Distribution Generalization

arXiv: https://arxiv.org/abs/2504.00850 · HTML: https://mlsanity.com/paper/2504.00850/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [FedProc: Prototypical Contrastive Federated Learning on Non-IID data](https://mlsanity.com/paper/2109.12273/) (2021)
- [Causal Attention for Unbiased Visual Recognition](https://mlsanity.com/paper/2108.08782/) (2021)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer](https://mlsanity.com/paper/2210.00912/) (2022)
- [Plug-in Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2401.02913/) (2024)
- [Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning](https://mlsanity.com/paper/2210.02326/) (2022)
- [Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2308.03457/) (2023)
- [Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity](https://mlsanity.com/paper/2405.16585/) (2024)
- [Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring](https://mlsanity.com/paper/2206.06818/) (2022)
- [StableFDG: Style and Attention Based Learning for Federated Domain Generalization](https://mlsanity.com/paper/2311.00227/) (2023)
- [Triple Sequence Learning for Cross-domain Recommendation](https://mlsanity.com/paper/2304.05027/) (2023)
- [Multi-source Domain Adaptation for Visual Sentiment Classification](https://mlsanity.com/paper/2001.03886/) (2020)
- [Meta-Causal Feature Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2208.10156/) (2022)
- [FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection](https://mlsanity.com/paper/2410.11397/) (2024)
- [Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization](https://mlsanity.com/paper/2401.10272/) (2024)
- [Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning](https://mlsanity.com/paper/2308.04142/) (2023)
- [Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation](https://mlsanity.com/paper/2307.02761/) (2023)

## Built on by (most cited first, 3 of 3)

- [Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2505.04979/) (2025)
- [Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning](https://mlsanity.com/paper/2509.22700/) (2025)
- [Geometric Prior-Guided Federated Prompt Calibration](https://mlsanity.com/paper/2512.07208/) (2025)

## Abstract (arXiv)

Attribute skew in federated learning leads local models to focus on learning non-causal associations, guiding them towards inconsistent optimization directions, which inevitably results in performance degradation and unstable convergence. Existing methods typically leverage data augmentation to enhance sample diversity or employ knowledge distillation to learn invariant representations. However, the instability in the quality of generated data and the lack of domain information limit their performance on unseen samples. To address these issues, this paper presents a global intervention and distillation method, termed FedGID, which utilizes diverse attribute features for backdoor adjustment to break the spurious association between background and label. It includes two main modules, where the global intervention module adaptively decouples objects and backgrounds in images, injects background information into random samples to intervene in the sample distribution, which links backgrounds to all categories to prevent the model from treating background-label associations as causal. The global distillation module leverages a unified knowledge base to guide the representation learning of client models, preventing local models from overfitting to client-specific attributes. Experimental results on three datasets demonstrate that FedGID enhances the model's ability to focus on the main subjects in unseen data and outperforms existing methods in collaborative modeling.