Preprint / Version 1

Enhancing AI mentorship with Tree of Thoughts and Federated Learning: a privacy-preserving approach




Application of Artificial Intelligence, Decision Tree, Distributed Computing


As the integration of AI becomes increasingly prevalent in our daily lives, there is a need to enhance the capabilities of AI - human interactions. In this study, we propose an innovative approach that combines two existing technologies, the Tree of Thoughts (ToT) framework and federated learning, to elevate the effectiveness of AI mentors. The ToT framework enriches the problem-solving abilities of AI mentors by enabling them to engage in more effective thinking processes. Simultaneously, federated learning enables AI mentors to learn collaboratively from each other while ensuring the privacy and security of user data by storing it securely on the user's device. This integrated approach empowers AI mentors to become smarter and more personalized, while safeguarding the privacy and confidentiality of personal information. By leveraging the synergistic benefits of the ToT framework and federated learning, our approach offers a robust solution for enhancing AI mentorship in a privacy-preserving manner.


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