Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

Abstract

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner. Yet, in most applications of FL, such as keyboard prediction, labeling data requires virtually no additional efforts, which is not generally the case. In reality, acquiring large-scale labeled datasets can be extremely costly, which motivates research works that exploit unlabeled data to help build machine learning models. However, to the best of our knowledge, few existing works aim to utilize unlabeled data to enhance federated learning, which leaves a potentially promising research topic. In this paper, we identify the need to exploit unlabeled data in FL, and survey possible research fields that can contribute to the goal.

References in corpus (24)

• Distilling the Knowledge in a Neural Network
• Semi-Supervised Classification with Graph Convolutional Networks
• mixup: Beyond Empirical Risk Minimization
• Learning Transferable Features with Deep Adaptation Networks
• Unsupervised Data Augmentation for Consistency Training
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• Temporal Ensembling for Semi-Supervised Learning
• Federated Learning for Mobile Keyboard Prediction
• Towards Federated Learning at Scale: System Design
• Secure Federated Transfer Learning
• MixMatch: A Holistic Approach to Semi-Supervised Learning
• Semi-Supervised Learning with Generative Adversarial Networks
• Applied Federated Learning: Improving Google Keyboard Query Suggestions
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
• SecureBoost: A Lossless Federated Learning Framework
• Federated Adversarial Domain Adaptation
• Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
• Federated Learning Of Out-Of-Vocabulary Words
• Federated Learning with Matched Averaging
• Active Federated Learning
• Selfie: Self-supervised Pretraining for Image Embedding
• Salvaging Federated Learning by Local Adaptation
• Practical Federated Gradient Boosting Decision Trees

Cited by in corpus (9)

• Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
• SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
• Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
• FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
• Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
• FedSiam: Towards Adaptive Federated Semi-Supervised Learning
• Federated Semi-Supervised Learning with Class Distribution Mismatch
• FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning
• Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt