mlsanityData to 7 Oct 2026

SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

arXiv 2203.08176 ↗First posted 15 Mar 2022cs.LG cs.AI cs.CRcited by 7

Not on the map. Why?

Abstract (arXiv)

Recent advances in wearable devices and Internet-of-Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users. Concerns over data privacy and communication costs also prohibit centralized data accumulation and training. We propose SemiPFL that supports edge users having no label or limited labeled datasets and a sizable amount of unlabeled data that is insufficient to train a well-performing model. In this work, edge users collaborate to train a Hyper-network in the server, generating personalized autoencoders for each user. After receiving updates from edge users, the server produces a set of base models for each user, which the users locally aggregate them using their own labeled dataset. We comprehensively evaluate our proposed framework on various public datasets from a wide range of application scenarios, from wearable health to IoT, and demonstrate that SemiPFL outperforms state-of-art federated learning frameworks under the same assumptions regarding user performance, network footprint, and computational consumption. We also show that the solution performs well for users without label or having limited labeled datasets and increasing performance for increased labeled data and number of users, signifying the effectiveness of SemiPFL for handling data heterogeneity and limited annotation. We also demonstrate the stability of SemiPFL for handling user hardware resource heterogeneity in three real-time scenarios.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 35 of its 83 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

35 of 83 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2016HyperNetworkscited by 1,003
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2019Federated Learning with Personalization Layerscited by 533
  10. 2021Towards Personalized Federated Learningcited by 462
  11. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  12. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
23 more
  1. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  2. 2021Personalized Federated Learning using Hypernetworkscited by 210
  3. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  4. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 154
  5. 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
  6. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  7. 2016Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearablescited by 93
  8. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  9. 2021Personalized Federated Learning with Gaussian Processescited by 76
  10. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  11. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  12. 2020Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligencecited by 39
  13. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  14. 2021SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Datacited by 31
  15. 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 29
  16. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  17. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  18. 2022Federated Learning Challenges and Opportunities: An Outlookcited by 19
  19. 2020Semi-supervised Federated Learning for Activity Recognitioncited by 18
  20. 2021New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learningcited by 11
  21. 2021Personalized Semi-Supervised Federated Learning for Human Activity Recognitioncited by 9
  22. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
  23. 2021FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcarecited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 4; 2026: 1.

7 papers.

  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2025Dynamic Tsetlin Machine Accelerators for On-Chip Training at the Edge using FPGAscited by 2
  3. 2023Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textilecited by 1
  4. 2025Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learningcited by 1
  5. 2025Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmationcited by 0
  6. 2025Semi-Supervised Online Learning on the Edge by Transforming Knowledge from Teacher Modelscited by 0
  7. 2026FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharingcited by 0