mlsanityData to 14 Oct 2026

FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

arXiv 2110.07829 ↗First posted 15 Oct 2021cs.LGcited by 4

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Abstract (arXiv)

Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years. The majority of existing works focus on supervised learning (SL) problems where it is assumed that clients carry labeled datasets while the server has no data. However, in realistic scenarios, clients are often unable to label their data due to the lack of expertise and motivation while the server may host a small amount of labeled data. How to reasonably utilize the server labeled data and the clients' unlabeled data is thus of paramount practical importance. In this paper, we propose a new FL algorithm, called FedSEAL, to solve this Semi-Supervised Federated Learning (SSFL) problem. Our algorithm utilizes self-ensemble learning and complementary negative learning to enhance both the accuracy and the efficiency of clients' unsupervised learning on unlabeled data, and orchestrates the model training on both the server side and the clients' side. Our experimental results on Fashion-MNIST and CIFAR10 datasets in the SSFL setting validate the effectiveness of our method, which outperforms the state-of-the-art SSFL methods by a large margin.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

22 of 32 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  4. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  8. 2018Federated Learning with Non-IID Datacited by 1,108
  9. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  10. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  11. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 920
  12. 2019Towards Federated Learning at Scale: System Designcited by 832
10 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  3. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  4. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  5. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  6. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  7. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 90
  8. 2020Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospectivecited by 27
  9. 2020Semi-supervised Federated Learning for Activity Recognitioncited by 18
  10. 2021A Federated Semi-Supervised Learning Approach for Network Traffic Classificationcited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTcited by 6
  2. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  3. 2023Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detectioncited by 4
  4. 2024Who Said What? An Automated Approach to Analyzing Speech in Preschool Classroomscited by 3