mlsanityData to 7 Oct 2026

FRuDA: Framework for Distributed Adversarial Domain Adaptation

arXiv 2112.13381 ↗First posted 26 Dec 2021cs.LGcited by 8

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Abstract (arXiv)

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is a lack of research on how uDA algorithms, particularly those based on adversarial learning, can work in distributed settings. In real-world applications, target domains are often distributed across thousands of devices, and existing adversarial uDA algorithms -- which are centralized in nature -- cannot be applied in these settings. To solve this important problem, we introduce FRuDA: an end-to-end framework for distributed adversarial uDA. Through a careful analysis of the uDA literature, we identify the design goals for a distributed uDA system and propose two novel algorithms to increase adaptation accuracy and training efficiency of adversarial uDA in distributed settings. Our evaluation of FRuDA with five image and speech datasets show that it can boost target domain accuracy by up to 50% and improve the training efficiency of adversarial uDA by at least 11 times.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 26 Dec 2021.

Builds on

25 of 45 references.

References · 25

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  4. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  5. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 844
  8. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
  9. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  10. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  11. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 311
  12. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
13 more
  1. 2018DLOW: Domain Flow for Adaptation and Generalizationcited by 176
  2. 2017Asynchronous Decentralized Parallel Stochastic Gradient Descentcited by 159
  3. 2016Correlation Alignment for Unsupervised Domain Adaptationcited by 146
  4. 2018D2: Decentralized Training over Decentralized Datacited by 140
  5. 2019Federated Adversarial Domain Adaptationcited by 140
  6. 2020Towards Inheritable Models for Open-Set Domain Adaptationcited by 64
  7. 2019Domain Adaptation with Asymmetrically-Relaxed Distribution Alignmentcited by 61
  8. 2020Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Datacited by 60
  9. 2017Incremental Adversarial Domain Adaptation for Continually Changing Environmentscited by 52
  10. 2019Transfer Learning with Dynamic Distribution Adaptationcited by 40
  11. 2018Deep Transfer Learning for Cross-domain Activity Recognitioncited by 25
  12. 2020Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systemscited by 11
  13. 2020Multi-source Attention for Unsupervised Domain Adaptationcited by 3

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Cited by year

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

8 papers.

  1. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  2. 2023Federated Learning without Full Labels: A Surveycited by 12
  3. 2024Balancing Similarity and Complementarity for Federated Learningcited by 7
  4. 2024Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Datacited by 4
  5. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  6. 2024Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexingcited by 0
  7. 2025Clustered Federated Learning via Embedding Distributionscited by 0
  8. 2025Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Modelscited by 0