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L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning

arXiv 2307.07393 ↗First posted 14 Jul 2023cs.CVcited by 29

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Abstract (arXiv)

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated learning (FL) into one coherent system can potentially offer data privacy guarantees while also advancing the quality and robustness of the learned visual representations without needing to move data around. However, client bias and divergence during FL aggregation caused by data heterogeneity limits the performance of learned visual representations on downstream tasks. In this paper, we propose a new aggregation strategy termed Layer-wise Divergence Aware Weight Aggregation (L-DAWA) to mitigate the influence of client bias and divergence during FL aggregation. The proposed method aggregates weights at the layer-level according to the measure of angular divergence between the clients' model and the global model. Extensive experiments with cross-silo and cross-device settings on CIFAR-10/100 and Tiny ImageNet datasets demonstrate that our methods are effective and obtain new SOTA performance on both contrastive and non-contrastive SSL approaches.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 44 of its 55 references that are in the map. First posted on arXiv 14 Jul 2023.

Builds on

44 of 55 references.

References · 44

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Citing papers per year: 2023: 1; 2024: 12; 2025: 12; 2026: 4.

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  1. 2024LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinementcited by 37
  2. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
  3. 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectorscited by 19
  4. 2024FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detectioncited by 14
  5. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  6. 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Datacited by 12
  7. 2024FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Datacited by 7
  8. 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinkingcited by 6
  9. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  10. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  11. 2024Exploring Federated Self-Supervised Learning for General Purpose Audio Understandingcited by 2
  12. 2024FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clientscited by 2
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  1. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 2
  2. 2025FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learningcited by 2
  3. 2024Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneitycited by 1
  4. 2025Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentationcited by 1
  5. 2025FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understandingcited by 1
  6. 2025IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learningcited by 1
  7. 2025Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insightscited by 1
  8. 2025CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environmentscited by 1
  9. 2026Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Datacited by 1
  10. 2024FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shiftingcited by 0
  11. 2024FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learningcited by 0
  12. 2025Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learningcited by 0
  13. 2025Cooperative Pseudo Labeling for Unsupervised Federated Classificationcited by 0
  14. 2025Towards Heterogeneous Quantum Federated Learning: Challenges and Solutionscited by 0
  15. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  16. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0
  17. 2026FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classificationcited by 0