mlsanityData to 14 Oct 2026

LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

arXiv 2008.03371 ↗First posted 7 Aug 2020cs.LG cs.DC stat.MLcited by 60

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Abstract (arXiv)

Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the participants as many as possible. The technology is rapidly advancing with many unsolved challenges, among which statistical heterogeneity (i.e., non-IID) and communication efficiency are two critical ones that hinder the development of federated learning. In this work, we propose LotteryFL -- a personalized and communication-efficient federated learning framework via exploiting the Lottery Ticket hypothesis. In LotteryFL, each client learns a lottery ticket network (i.e., a subnetwork of the base model) by applying the Lottery Ticket hypothesis, and only these lottery networks will be communicated between the server and clients. Rather than learning a shared global model in classic federated learning, each client learns a personalized model via LotteryFL; the communication cost can be significantly reduced due to the compact size of lottery networks. To support the training and evaluation of our framework, we construct non-IID datasets based on MNIST, CIFAR-10 and EMNIST by taking feature distribution skew, label distribution skew and quantity skew into consideration. Experiments on these non-IID datasets demonstrate that LotteryFL significantly outperforms existing solutions in terms of personalization and communication cost.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2020.

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Citing papers per year: 2021: 13; 2022: 9; 2023: 15; 2024: 13; 2025: 4; 2026: 6.

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  1. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  3. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  4. 2021Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Bettercited by 55
  5. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  6. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  7. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 36
  8. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  9. 2022Rare Gems: Finding Lottery Tickets at Initializationcited by 34
  10. 2022Sparse Random Networks for Communication-Efficient Federated Learningcited by 32
  11. 2022Achieving Personalized Federated Learning with Sparse Local Modelscited by 31
  12. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
38 more
  1. 2024A Survey of Lottery Ticket Hypothesiscited by 24
  2. 2023Phoenix: A Federated Generative Diffusion Modelcited by 22
  3. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  4. 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancercited by 17
  5. 2022Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?cited by 16
  6. 2022Self-Aware Personalized Federated Learningcited by 16
  7. 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
  8. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  9. 2022Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edgecited by 12
  10. 2023Complement Sparsification: Low-Overhead Model Pruning for Federated Learningcited by 12
  11. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
  12. 2021New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learningcited by 11
  13. 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregationcited by 11
  14. 2021Communication-Efficient and Personalized Federated Lottery Ticket Learningcited by 10
  15. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  16. 2021FRL: Federated Rank Learningcited by 8
  17. 2021Constrained Differentially Private Federated Learning for Low-bandwidth Devicescited by 6
  18. 2023Adaptive Compression in Federated Learning via Side Informationcited by 6
  19. 2023Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computingcited by 5
  20. 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
  21. 2021FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Updatecited by 4
  22. 2023PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learningcited by 4
  23. 2021Unifying Distillation with Personalization in Federated Learningcited by 3
  24. 2022E2FL: Equal and Equitable Federated Learningcited by 3
  25. 2023FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learningcited by 2
  26. 2023Federated Fine-Tuning of Foundation Models via Probabilistic Maskingcited by 2
  27. 2024Gradient-Congruity Guided Federated Sparse Trainingcited by 2
  28. 2024FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learningcited by 2
  29. 2024Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Modelscited by 2
  30. 2026Towards Building Non-Fine-Tunable Foundation Modelscited by 2
  31. 2021Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesiscited by 1
  32. 2023SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Trainingcited by 1
  33. 2023Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approachcited by 1
  34. 2024SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learningcited by 1
  35. 2025Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneitycited by 1
  36. 2021FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learningcited by 0
  37. 2022One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Designcited by 0
  38. 2023FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learningcited by 0

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