Definition ∞ Model Weight Updates are adjustments made to the parameters or “weights” of an artificial intelligence or machine learning model during its training or refinement process. These updates are essential for improving the model’s performance and accuracy as it learns from new data. In decentralized machine learning, these updates can be collaboratively determined and verified by multiple participants, often through cryptographic methods. The precise management of these updates is fundamental to the model’s overall effectiveness and learning capacity.
Context ∞ The management of model weight updates is a central concern in federated learning and decentralized AI systems, particularly regarding privacy and integrity. Ensuring that these updates are accurate, unbiased, and resistant to manipulation is critical for the reliability of the collective model. Future developments focus on optimizing these update mechanisms to balance efficiency, security, and data privacy across distributed networks.
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