mlsanityData to 7 Oct 2026

FedDAR: Federated Domain-Aware Representation Learning

arXiv 2209.04007 ↗First posted 8 Sep 2022cs.LGcited by 8

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Abstract (arXiv)

Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients. However, the latent relationships between clients' data are ignored. In this work, we focus on a special non-iid FL problem, called Domain-mixed FL, where each client's data distribution is assumed to be a mixture of several predefined domains. Recognizing the diversity of domains and the similarity within domains, we propose a novel method, FedDAR, which learns a domain shared representation and domain-wise personalized prediction heads in a decoupled manner. For simplified linear regression settings, we have theoretically proved that FedDAR enjoys a linear convergence rate. For general settings, we have performed intensive empirical studies on both synthetic and real-world medical datasets which demonstrate its superiority over prior FL methods.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 49 of its 70 references that are in the map. First posted on arXiv 8 Sep 2022.

Builds on

49 of 70 references.

References · 49

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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37 more
  1. 2019Federated Learning with Personalization Layerscited by 533
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  37. 2021Federating for Learning Group Fair Modelscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 2.

8 papers.

  1. 2024Federated Representation Learning in the Under-Parameterized Regimecited by 7
  2. 2023PPFL: A Personalized Federated Learning Framework for Heterogeneous Populationcited by 1
  3. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  4. 2025DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learningcited by 1
  5. 2026FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devicescited by 1
  6. 2023Global Layers: Non-IID Tabular Federated Learningcited by 0
  7. 2023FedL2P: Federated Learning to Personalizecited by 0
  8. 2026FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidancecited by 0