Model parameters are the configurable values or settings that define the behavior and characteristics of a computational model or algorithm. In the context of digital assets, these might include interest rates in a lending protocol, reward distribution rules in a staking mechanism, or risk thresholds in a financial application. Adjusting these parameters significantly alters the system’s output. They are critical for system tuning.
Context
The careful selection and adjustment of model parameters are central to the stability and economic viability of decentralized finance protocols. Debates often concern the optimal governance mechanisms for modifying these parameters to adapt to changing market conditions or security needs. Future protocol designs may incorporate more adaptive or autonomous parameter adjustment systems.
ZKPoT consensus leverages zk-SNARKs to cryptographically verify model contribution accuracy without revealing sensitive training data, enabling trustless federated learning.
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