mlsanityData to 7 Oct 2026

Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

arXiv 2105.07066 ↗First posted 14 May 2021cs.LG cs.AIcited by 39

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Abstract (arXiv)

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. The unbiasedness of the proposed FedPNS design is illustrated and the convergence rate improvement of FedPNS over the commonly adopted Federated Averaging (FedAvg) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 14 May 2021.

Builds on

21 of 31 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  7. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  10. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  11. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  12. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
9 more
  1. 2020Optimal Client Sampling for Federated Learningcited by 123
  2. 2018Wireless Network Intelligence at the Edgecited by 90
  3. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  4. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  5. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  6. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  7. 2020Fast-Convergent Federated Learningcited by 61
  8. 2020Optimal Importance Sampling for Federated Learningcited by 20
  9. 2017When Mobile Blockchain Meets Edge Computingcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 12; 2024: 10; 2025: 6; 2026: 3.

39 papers.

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  1. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  2. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  3. 2022Meta Knowledge Condensation for Federated Learningcited by 26
  4. 2023MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligencecited by 20
  5. 2022Federated Learning with Flexible Controlcited by 12
  6. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  7. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  8. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  9. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  10. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  11. 2023Improving Accelerated Federated Learning with Compression and Importance Samplingcited by 7
  12. 2025Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocationcited by 7
27 more
  1. 2025Federated Learning for Diffusion Modelscited by 6
  2. 2024FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneitycited by 5
  3. 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
  4. 2022FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgmentcited by 3
  5. 2022Combating Client Dropout in Federated Learning via Friend Model Substitutioncited by 3
  6. 2022Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimizationcited by 3
  7. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  8. 2023Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosiscited by 2
  9. 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHAcited by 2
  10. 2023Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Networkcited by 2
  11. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  12. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  13. 2023An Empirical Study of Efficiency and Privacy of Federated Learning Algorithmscited by 1
  14. 2024HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learningcited by 1
  15. 2025FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selectioncited by 1
  16. 2025AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregatorcited by 1
  17. 2022Federated Latent Class Regression for Hierarchical Datacited by 0
  18. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  19. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  20. 2023Personalized Federated Learning with Contextual Modulation and Meta-Learningcited by 0
  21. 2024FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learningcited by 0
  22. 2024FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selectioncited by 0
  23. 2024GAI-Enabled Explainable Personalized Federated Semi-Supervised Learningcited by 0
  24. 2025Random Client Selection on Contrastive Federated Learning for Tabular Datacited by 0
  25. 2026Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagerycited by 0
  26. 2026Personalized Federated Learning via Gaussian Generative Modelingcited by 0
  27. 2026Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Cachingcited by 0