A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning

• URL: http://arxiv.org/abs/2503.09833v1
• Date: Wed, 12 Mar 2025 20:54:22 GMT
• Title: A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
• Authors: Sarwar Saif, Md Jahirul Islam, Md. Zihad Bin Jahangir, Parag Biswas, Abdur Rashid, MD Abdullah Al Nasim, Kishor Datta Gupta,
• Abstract summary: The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data.<n>Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages.<n>We looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources.<n>Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.

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