mlsanityData to 7 Oct 2026

FedGen: Generalizable Federated Learning for Sequential Data

arXiv 2211.01914 ↗First posted 3 Nov 2022cs.LG cs.AIcited by 9

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Abstract (arXiv)

Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data. In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues on distributed devices or clients that can erroneously influence models. Current generalization approaches are designed for centralized training and attempt to identify features that have an invariant causal relationship with the target, thereby reducing the effect of spurious features. However, such invariant risk minimization approaches rely on apriori knowledge of training data distributions which is hard to obtain in many applications. In this work, we present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features in a collaborative manner without prior knowledge of training distributions. We evaluate our approach on real-world datasets from different domains and show that FedGen results in models that achieve significantly better generalization and can outperform the accuracy of current federated learning approaches by over 24%.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

36 of 56 references.

References · 36

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24 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 4.

9 papers.

  1. 2023Federated Domain Generalization: A Surveycited by 25
  2. 2022DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuningcited by 9
  3. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8
  4. 2025PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learningcited by 8
  5. 2024Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuningcited by 6
  6. 2024The Key of Parameter Skew in Federated Learningcited by 0
  7. 2025Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penaltycited by 0
  8. 2025HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrastcited by 0
  9. 2025Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsitycited by 0