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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  1. Verification of Computation

Blockchain Audit

Before the emergence of blockchain technology, early explorations were undertaken to construct audit-based solutions\cite{belenkiy2008incentivizing}. These solutions relied on trusted clients to recompute sampled tasks performed by untrusted workers, employing incentive mechanisms to reward honest work and penalize cheating.

The advent of blockchain technology, notably underlying Bitcoin\cite{nakamoto2008bitcoin}, brought about revolutionary features such as immutability and traceability in decentralized data storage.

In the realm of federated learning, blockchain has been harnessed to ensure data provenance and maintain auditable blocks \cite{bao2019flchain, chen2018machine, li2020blockchain, qu2022blockchain}. This integration ensures the verification of learning processes, enabling validators to scrutinize results. Additionally, the incentive mechanisms inherent in blockchain ecosystems incentivize nodes to contribute high-quality data and services to decentralized systems.

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Last updated 11 months ago