mlsanityData to 7 Oct 2026

FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data

arXiv 2205.09305 ↗First posted 19 May 2022cs.LG cs.AI cs.DCcited by 4

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Abstract (arXiv)

Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos. Though successfully possessing advantages in both scale and privacy, federated learning is hurt by domain shift problems, where the learning models are unable to generalize to unseen domains whose data distribution is non-i.i.d. with respect to the training domains. In this study, we propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies of environments and unravel the domain shift problems in federated networks. The benchmark and real-world dataset experiments bring evidence that our proposed algorithm outperforms conventional baselines and similar federated learning algorithms. This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT). The code is released at https://github.com/mikemikezhu/FedILC.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

32 of 47 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Invariant Risk Minimizationcited by 1,832
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  9. 2019Federated Learning with Personalization Layerscited by 533
  10. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  11. 2019Fair Resource Allocation in Federated Learningcited by 389
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
20 more
  1. 2020Gradient Starvation: A Learning Proclivity in Neural Networkscited by 262
  2. 2021Gradient Matching for Domain Generalizationcited by 258
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  4. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  5. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  6. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  7. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  8. 2020Learning explanations that are hard to varycited by 132
  9. 2021Group Fisher Pruning for Practical Network Compressioncited by 130
  10. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  11. 2018An introduction to domain adaptation and transfer learningcited by 112
  12. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  13. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  14. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  15. 2021SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalizationcited by 50
  16. 2020DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentationcited by 48
  17. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  18. 2020FedFMC: Sequential Efficient Federated Learning on Non-iid Datacited by 17
  19. 2021ViViT: Curvature access through the generalized Gauss-Newton's low-rank structurecited by 16
  20. 2019Adaptive norms for deep learning with regularized Newton methodscited by 5

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023Federated Domain Generalization: A Surveycited by 25
  3. 2022Parameter-Parallel Distributed Variational Quantum Algorithmcited by 0
  4. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0