mlsanityData to 7 Oct 2026

FedNS: Improving Federated Learning for collaborative image classification on mobile clients

arXiv 2101.07995 ↗First posted 20 Jan 2021cs.CVcited by 3

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Abstract (arXiv)

Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popular FL algorithm is Federated Averaging (FedAvg), which is based on taking weighted average of the client models, with the weights determined largely based on dataset sizes at the clients. In this paper, we propose a new approach, termed Federated Node Selection (FedNS), for the server's global model aggregation in the FL setting. FedNS filters and re-weights the clients' models at the node/kernel level, hence leading to a potentially better global model by fusing the best components of the clients. Using collaborative image classification as an example, we show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 20 Jan 2021.

Builds on

13 of 24 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2018Federated Learning with Non-IID Datacited by 1,099
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  10. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  11. 2019Natural Compression for Distributed Deep Learningcited by 88
  12. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
1 more
  1. 2020Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learningcited by 37

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillationcited by 19
  2. 2024FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissionscited by 0
  3. 2025Balancing Client Participation in Federated Learning Using AoIcited by 0