mlsanityData to 14 Oct 2026

Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning

arXiv 2210.02326 ↗First posted 5 Oct 2022cs.CV cs.LGcited by 35

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Abstract (arXiv)

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealistically assume labeled data in the remote clients. Here we propose a novel task (FFREEDA) in which the clients' data is unlabeled and the server accesses a source labeled dataset for pre-training only. To solve FFREEDA, we propose LADD, which leverages the knowledge of the pre-trained model by employing self-supervision with ad-hoc regularization techniques for local training and introducing a novel federated clustered aggregation scheme based on the clients' style. Our experiments show that our algorithm is able to efficiently tackle the new task outperforming existing approaches. The code is available at https://github.com/Erosinho13/LADD.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 60 of its 76 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

60 of 76 references.

References · 60

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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  12. 2016Pyramid Scene Parsing Networkcited by 2,479
38 more
  1. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  5. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,209
  6. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  7. 2019Towards Federated Learning at Scale: System Designcited by 832
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  10. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  11. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
  12. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 600
  13. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  14. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  15. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  16. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  17. 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 541
  18. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  19. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  20. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 314
  21. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 300
  22. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  23. 2021Escaping the Big Data Paradigm with Compact Transformerscited by 264
  24. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  25. 2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
  26. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  27. 2019Federated Adversarial Domain Adaptationcited by 140
  28. 2017No More Discrimination: Cross City Adaptation of Road Scene Segmenterscited by 111
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  34. 2021Federated Multi-Target Domain Adaptationcited by 44
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 9; 2024: 10; 2025: 9; 2026: 5.

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  1. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  2. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  3. 2023Asynchronous Federated Continual Learningcited by 29
  4. 2023Federated Domain Generalization: A Surveycited by 25
  5. 2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
  6. 2023Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformerscited by 8
  7. 2024Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learningcited by 8
  8. 2023Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learningcited by 7
  9. 2022Clustered Federated Learning based on Nonconvex Pairwise Fusioncited by 4
  10. 2024When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weathercited by 4
  11. 2024Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Datacited by 4
  12. 2025Federated Out-of-Distribution Generalization: A Causal Augmentation Viewcited by 4
23 more
  1. 2025Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalizationcited by 4
  2. 2023FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Drivingcited by 3
  3. 2025Global Intervention and Distillation for Federated Out-of-Distribution Generalizationcited by 3
  4. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  5. 2024Collaborative Visual Place Recognition through Federated Learningcited by 2
  6. 2025FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Modelscited by 2
  7. 2023FACT: Federated Adversarial Cross Trainingcited by 1
  8. 2023Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisitioncited by 1
  9. 2024Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replaycited by 1
  10. 2024FedPartWhole: Federated domain generalization via consistent part-whole hierarchiescited by 1
  11. 2024Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costscited by 0
  12. 2024PARDON: Privacy-Aware and Robust Federated Domain Generalizationcited by 0
  13. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  14. 2025Federated Unsupervised Semantic Segmentationcited by 0
  15. 2025Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Modelscited by 0
  16. 2025FRIEREN: Federated Learning with Vision-Language Regularization for Segmentationcited by 0
  17. 2025Gains: Fine-grained Federated Domain Adaptation in Open Setcited by 0
  18. 2025FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptationcited by 0
  19. 2026Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generationcited by 0
  20. 2026UCBound-Net: Uncertainty-Guided Boundary-Aware Continual Learning for Domain-Incremental Ultrasound Segmentationcited by 0
  21. 2026EFFEKT: Efficient Federated Knowledge Transfer to Foundation Modelscited by 0
  22. 2026When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimationcited by 0
  23. 2026Multi-Scale Temporal Domain Alignment for Federated Video Domain Adaptationcited by 0