Decentralized Training refers to the process of developing and refining artificial intelligence models across a distributed network of participants or devices, rather than on a centralized server. In this approach, data remains localized, and only model updates or aggregated parameters are shared. This method enhances data privacy, reduces reliance on single points of failure, and can leverage diverse computational resources.
Context
The application of Decentralized Training is gaining traction in contexts where data privacy and ownership are paramount, such as in Web3 and federated learning applications. A key challenge involves designing secure and efficient aggregation mechanisms for model parameters without compromising individual data. Future research will focus on improving the robustness and fairness of these distributed learning environments.
A new ZKPoT mechanism uses zk-SNARKs to validate machine learning model contributions privately, resolving the efficiency and privacy conflict in blockchain-secured AI.
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