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The use of artificial intelligence (AI) into financial prediction models has become more important in the context of the continuously changing environment of the financial industry. The conventional centralised artificial intelligence models are dependent on enormous volumes of centralised data, which might provide issues in terms of data privacy and security, as well as single points of failure. Specifically with regard to the field of financial forecasting, this article investigates the idea of decentralised artificial intelligence as a novel approach to addressing the issues that have been presented. The creation of models that do not depend on a single central authority or data repository is the goal of decentralised artificial intelligence, which makes use of distributed computing and blockchain technology. This method improves data privacy by allowing data to stay on local devices while still contributing to model training and predictions. This also allows for the data to be stored locally. Due to the fact that the model is trained over a network of nodes rather than a single centralised server, it also decreases the dangers that are connected with data breaches and manipulation.
Arulkumaran et al. (Tue,) studied this question.