mlsanityData to 14 Oct 2026

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

arXiv 2311.06750 ↗First posted 12 Nov 2023cs.LG cs.AIcited by 99

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Abstract (arXiv)

Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. Firstly, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/WenkeHuang/MarsFL.

Key facts. Cited by 99 papers in the map (data through 14 Oct 2026). Builds on 171 of its 309 references that are in the map. First posted on arXiv 12 Nov 2023.

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171 of 309 references.

References · 171

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  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
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Citing papers per year: 2023: 2; 2024: 19; 2025: 49; 2026: 29.

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  1. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  2. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 30
  3. 2025The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutionscited by 26
  4. 2024All in One Framework for Multimodal Re-identification in the Wildcited by 18
  5. 2025Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Modelcited by 16
  6. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  7. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  8. 2024Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalizationcited by 12
  9. 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learningcited by 12
  10. 2025LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Modelscited by 11
  11. 2024Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identificationcited by 10
  12. 2023CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Datacited by 9
38 more
  1. 2024SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overheadcited by 9
  2. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 8
  3. 2025SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devicescited by 8
  4. 2024FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Datacited by 7
  5. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  6. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  7. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
  8. 2025FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learningcited by 5
  9. 2025Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clientscited by 5
  10. 2025From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identificationcited by 5
  11. 2025ProFed: a Benchmark for Proximity-based non-IID Federated Learningcited by 5
  12. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4
  13. 2025FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Datacited by 4
  14. 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learningcited by 4
  15. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
  16. 2024Communication-Efficient Federated Group Distributionally Robust Optimizationcited by 3
  17. 2024FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Predictioncited by 3
  18. 2024Masked Autoencoders are Parameter-Efficient Federated Continual Learnerscited by 3
  19. 2025AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learningcited by 3
  20. 2025FedEAT: A Robustness Optimization Framework for Federated LLMscited by 3
  21. 2025An Empirical Study of Federated Prompt Learning for Vision Language Modelcited by 3
  22. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  23. 2024FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completenesscited by 2
  24. 2024FPPL: An Efficient and Non-IID Robust Federated Continual Learning Frameworkcited by 2
  25. 2025FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysiscited by 2
  26. 2025Generalization in Federated Learning: A Conditional Mutual Information Frameworkcited by 2
  27. 2025FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysiscited by 2
  28. 2025FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Modelscited by 2
  29. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  30. 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learningcited by 2
  31. 2025Geometric Prior-Guided Federated Prompt Calibrationcited by 2
  32. 2024Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Securitycited by 1
  33. 2025FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Modelscited by 1
  34. 2025Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decouplingcited by 1
  35. 2025pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimizationcited by 1
  36. 2025FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Modelscited by 1
  37. 2025FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attackscited by 1
  38. 2025Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insightscited by 1

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