Notes
Network Securities and Cryptoeconomic Aspects: The decentralization of AI model generation across various nodes requires robust security measures, especially when some nodes may be compromised or corrupted. It's crucial to ensure that nodes have a financial incentive to act honestly, maintaining the integrity of the system. A common method is staking, where nodes place deposits of utility tokens, which can be confiscated in cases of misbehavior. This incentive design, used in many blockchain implementations, requires coordinator nodes, which maintain the global ledger, to stake a significant amount of utility tokens to become a coordinator. Misbehaving results in the loss of their staked tokens, ensuring the security of the protocol's L1 layer. The number of tokens to be staked by different roles in the network depends on factors like the token value, expected rewards, risk of penalties, and the network's economic model. Service providers should stake an amount reflecting their commitment to providing quality services and accurate models, high enough to discourage fraud but not too high to prevent genuine providers. Validators should stake an amount showing their commitment to honest and accurate validations, substantial enough to prevent approving fraudulent claims or models but not so high as to create barriers for honest validators. Verifiers should stake a smaller amount compared to service providers and validators, as their primary role is to verify the validators' work.
Concurrent Roles: It's possible for nodes to assume different roles simultaneously to maintain the integrity and efficiency of the system. For instance, nodes with high GPU power can act as both validators and service providers. Generally, any node in the network should be capable of performing verification. It's absolutely essential to apply the verification algorithm to a model with multiple clusters of validations, as at least one cluster is guaranteed to be incorrect. However, a model with a single cluster of validations is highly likely to have been correctly validated.
Validation Definiteness: The validation process must yield consistent results, ensuring that for any given model and test data, the output remains constant across honest nodes, regardless of settings. This eliminates confusion in validation and verification procedures. The PoT implementation should always supply the validation function for adaptability and upgradability. Clients shouldn't provide their own validation functions to avoid inconsistencies; instead, they should select from available options. The network must be compatible with mainstream models like convolutional neural networks (CNN) and Long Short-Term Memory Network (LSTM), by incorporating validation functions for these models into the system's foundational layer.
Commitment Scheme: In decentralized training systems, a commitment scheme enables participants to commit to a generated model while keeping it hidden, with the ability to reveal it later. This is designed so a participant can't claim the model without committing to it earlier than the real owner in the global ledger. This has important applications in model ownership claim/verification and rewards distribution in PoT protocol implementations. The PoT.Claim algorithm involves two phases in the commitment scheme:
The commit phase: A participant trains a model and commits to it by broadcasting its signature to the network.
The reveal phase: The participant reveals the trained model, allowing others to validate its performance and verify the ownership claim.
With the commitment scheme, malicious service providers can't theoretically steal models, as they won't have the model's signature in the global ledger during the revealing phase.
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