Fair and efficient contribution valuation for vertical federated learning

Understand

Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data.

• Vertical federated learning, also known as feature-based federated learning, applies to scenarios where data sources have the same sample IDs but different feature sets.
• To ensure fairness among data owners, it is critical to objectively assess the contributions from different data sources and compensate the corresponding data owners accordingly.
• The Shapley value is a provably fair contribution valuation metric originating from cooperative game theory.