Federated Learning without Full Labels: A Survey

Understand

Data privacy has become an increasingly important concern in real-world big data applications such as machine learning.

• To address the problem, federated learning (FL) has been a promising solution to building effective machine learning models from decentralized and private data.
• Existing federated learning algorithms mainly tackle the supervised learning problem, where data are assumed to be fully labeled.
• However, in practice, fully labeled data is often hard to obtain, as the participants may not have sufficient domain expertise, or they lack the motivation and tools to label data.