mlsanityData to 14 Oct 2026

Federated Residual Learning

arXiv 2003.12880 ↗First posted 28 Mar 2020cs.LG stat.MLcited by 23

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Abstract (arXiv)

We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides. Our framework is robust to data heterogeneity, addressing the slow convergence problem traditional federated learning methods face when the data is non-i.i.d. across clients. We test the theory empirically and find substantial performance gains over baselines.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2020.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 5; 2024: 0; 2025: 2.

23 papers.

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  1. 2022Federated Learning with Partial Model Personalizationcited by 114
  2. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  3. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  4. 2021Federated Linear Contextual Banditscited by 61
  5. 2021Federated Multi-Armed Banditscited by 59
  6. 2021Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspectivecited by 38
  7. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  8. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  9. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  10. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  11. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  12. 2021Private Multi-Task Learning: Formulation and Applications to Federated Learningcited by 11
11 more
  1. 2021Communication-Efficient Agnostic Federated Averagingcited by 10
  2. 2021Practical and Secure Federated Recommendation with Personalized Maskscited by 7
  3. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
  4. 2022Multi-Model Federated Learning with Provable Guaranteescited by 5
  5. 2022Federated Online Sparse Decision Makingcited by 3
  6. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 3
  7. 2023Federated Learning for Heterogeneous Bandits with Unobserved Contextscited by 1
  8. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  9. 2022Vertical Federated Linear Contextual Banditscited by 0
  10. 2023Federated Offline Policy Learningcited by 0
  11. 2025Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsitycited by 0