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Device Heterogeneity in Federated Learning: A Superquantile Approach

arXiv 2002.11223 ↗First posted 25 Feb 2020stat.ML cs.DC cs.LGcited by 21

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Abstract (arXiv)

We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized superquantile-based objective, where the parameter ranges over levels of conformity. We present an optimization algorithm and establish its convergence to a stationary point. We show how to practically implement it using secure aggregation by interleaving iterations of the usual federated averaging method with device filtering. We conclude with numerical experiments on neural networks as well as linear models on tasks from computer vision and natural language processing.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Feb 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 5; 2025: 2; 2026: 4.

21 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
  3. 2023Correlated Noise Provably Beats Independent Noise for Differentially Private Learningcited by 23
  4. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  5. 2022Federated learning: Applications, challenges and future directionscited by 11
  6. 2024Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyondcited by 11
  7. 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
  8. 2023Scaff-PD: Communication Efficient Fair and Robust Federated Learningcited by 6
  9. 2023A Comprehensive Survey On Client Selections in Federated Learningcited by 6
  10. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  11. 2021OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learningcited by 2
  12. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
9 more
  1. 2025Convergence of Agnostic Federated Averagingcited by 2
  2. 2023DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLTcited by 0
  3. 2024Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shiftscited by 0
  4. 2024Federated Learning with Relative Fairnesscited by 0
  5. 2025Towards Performatively Stable Equilibria in Decision-Dependent Games for Arbitrary Data Distribution Mapscited by 0
  6. 2026Convergence of empirical subgradients for optimal transport-based objectivescited by 0
  7. 2026The Privacy-Hallucination Tradeoff in Differentially Private Language Modelscited by 0
  8. 2026FedeRage: Provably Convergent Agnostic Federated Learning under General Client Driftcited by 0
  9. 2026ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learningcited by 0