mlsanityData to 14 Oct 2026

FedDisco: Federated Learning with Discrepancy-Aware Collaboration

arXiv 2305.19229 ↗First posted 30 May 2023cs.LGcited by 67

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Abstract (arXiv)

This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of data heterogeneity. To alleviate this issue, most previous works consider either regularizing local models or fine-tuning the global model, while they ignore the adjustment of aggregation weights and simply assign weights based on the dataset size. However, based on our empirical observations and theoretical analysis, we find that the dataset size is not optimal and the discrepancy between local and global category distributions could be a beneficial and complementary indicator for determining aggregation weights. We thus propose a novel aggregation method, Federated Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation weights not only involve both the dataset size and the discrepancy value, but also contribute to a tighter theoretical upper bound of the optimization error. FedDisco also promotes privacy-preservation, communication and computation efficiency, as well as modularity. Extensive experiments show that our FedDisco outperforms several state-of-the-art methods and can be easily incorporated with many existing methods to further enhance the performance. Our code will be available at https://github.com/MediaBrain-SJTU/FedDisco.

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

34 of 47 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  8. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  9. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  10. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  11. 2018Federated Learning with Non-IID Datacited by 1,108
  12. 2020Adaptive Federated Optimizationcited by 1,046
22 more
  1. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  2. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  3. 2018LEAF: A Benchmark for Federated Settingscited by 792
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  6. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 669
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  10. 2021Deep Long-Tailed Learning: A Surveycited by 417
  11. 2019Fair Resource Allocation in Federated Learningcited by 394
  12. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  13. 2021A Field Guide to Federated Optimizationcited by 315
  14. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  15. 2022Multi-Task Learning as a Bargaining Gamecited by 215
  16. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  17. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  18. 2015A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classificationcited by 92
  19. 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networkscited by 58
  20. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27
  21. 2022FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregationcited by 16
  22. 2021Federated Submodel Optimization for Hot and Cold Data Featurescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 26; 2025: 22; 2026: 15.

67 papers.

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  1. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  2. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  3. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  4. 2024FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Modelscited by 30
  5. 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectorscited by 19
  6. 2024Federated Learning with Bilateral Curation for Partially Class-Disjoint Datacited by 13
  7. 2024Task Arithmetic Through The Lens Of One-Shot Federated Learningcited by 12
  8. 2024AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Modelscited by 11
  9. 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregationcited by 10
  10. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  11. 2024Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron Anchorscited by 7
  12. 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinkingcited by 7
38 more
  1. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  2. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
  3. 2024Lightweight Unsupervised Federated Learning with Pretrained Vision Language Modelcited by 5
  4. 2024Federated Learning under Partially Class-Disjoint Data via Manifold Reshapingcited by 4
  5. 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learningcited by 4
  6. 2023Federated Learning Empowered by Generative Contentcited by 3
  7. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memorycited by 3
  8. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 3
  9. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  10. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 3
  11. 2025FedSC: Federated Learning with Semantic-Aware Collaborationcited by 3
  12. 2025Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusioncited by 3
  13. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learningcited by 3
  14. 2025FedEL: Federated Elastic Learning for Heterogeneous Devicescited by 3
  15. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  16. 2026Diffusion-Guided Semantic Consistency for Multimodal Heterogeneitycited by 2
  17. 2023Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learningcited by 1
  18. 2024Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learningcited by 1
  19. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  20. 2024Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentationcited by 1
  21. 2024Caesar: A Low-deviation Compression Approach for Efficient Federated Learningcited by 1
  22. 2025Distribution-Aware Mobility-Assisted Decentralized Federated Learningcited by 1
  23. 2025FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaborationcited by 1
  24. 2025Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregationcited by 1
  25. 2026DeepAFL: Deep Analytic Federated Learningcited by 1
  26. 2026EmbTracker: Traceable Black-box Watermarking for Federated Language Modelscited by 1
  27. 2026FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learningcited by 1
  28. 2024FedGuCci: Making Local Models More Connected in Landscape for Federated Learningcited by 0
  29. 2024Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacycited by 0
  30. 2024FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learningcited by 0
  31. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  32. 2024Fast convergence of a Federated Expectation-Maximization Algorithmcited by 0
  33. 2024Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learningcited by 0
  34. 2024Personalized Hierarchical Split Federated Learning in Wireless Networkscited by 0
  35. 2025Optimize Incompatible Parameters through Compatibility-aware Knowledge Integrationcited by 0
  36. 2025Provably Near-Optimal Federated Ensemble Distillation with Negligible Overheadcited by 0
  37. 2025FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolutioncited by 0
  38. 2025Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learningcited by 0

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