DeAI: decentralized artificial intelligence
  • Introduction
    • General Terminology
  • Landscape
    • Data Providers
    • Computing Power
    • Model Training Task
    • Challenges
  • Privacy Preservation
    • Data Process
    • Privacy Preserved Training
    • Federated Learning
    • Cryptographic Computation
      • Homomorphic encryption
      • Multi-Party Computation
      • Trusted Execution Environment
    • Challenges
  • Security
    • Data Poisoning
    • Model Poisoning
    • Sybil Attacks
    • Impact of Large Models
    • Responsibility
  • Incentive mechanisms
    • Problem Formulation
    • Contribution Evaluation
    • Copyright
  • Verification of Computation
    • Computation on Smart Contract
    • Zero-Knowledge Proof
    • Blockchain Audit
    • Consensus Protocol
  • Network Scalability
    • Local Updating
    • Cryptography Protocol
    • Distribution Topology
    • Compression
    • Parameter-Efficient Fine Tuning
  • Conclusion
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Incentive mechanisms

Incentive mechanisms play a pivotal role in DeAI systems, not only in rewarding participants for superior performance but also in rendering attacks economically unviable. Effective incentive mechanisms has been extensively discussed in various decentralized contexts such as decentralized markets, peer-to-peer networks, computation resource management and crowdsensing platforms. Furthermore, the decentralized nature of DeAI allows for the design of specific incentive mechanisms tailored to particular use cases.

The incentive mechanisms in DeAI are primarily aimed at addressing two major challenges:

  • Motivate and maintain participants for their high performance.

  • Evaluate participants' contributions accurately and fairly.

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Last updated 11 months ago