Overcoming Trust and Incentive Barriers in Decentralized AI

Decentralized AI promises to unlock the potential of fragmented data across domains like healthcare and finance, but faces significant challenges in trust and incentive alignment. This paper proposes a comprehensive framework addressing these challenges through advanced privacy-preserving techniques and federated data markets. We integrate collaborative inference, split learning, and synthetic data sharing to establish trust, while leveraging federated data valuation and acquisition to foster fair and efficient data markets. Our systematic review identifies areas of progress in decentralized AI and highlights key research opportunities. This analysis focuses on overcoming barriers to collaborative AI development while preserving data sovereignty, paving the way for more inclusive and innovative AI ecosystems.