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BDCP Protocol Mainnet Launch: Revolutionizing of Decentralized Computing Introduction

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BDCP Protocol Mainnet Launch: Revolutionizing of Decentralized Computing Introduction

June 17
18:32 2024

The year 2024 witnessed the launch of the Bayesian Decentralized Computation Protocol (BDCP) mainnet, marking a significant leap forward in the fields of decentralized computing and supercomputing. BDCP not only addresses the issue of data storage but also completely transforms the use and realization of data value through distributed computing and machine learning. We will delve into various aspects of the BDCP protocol, showcasing how it operates within decentralized computing networks and providing insights into its future development prospects.

Origin and Definition of BDCP

BDCP, short for Bayesian Decentralized Computation Protocol, draws inspiration from the eminent statistician Thomas Bayes, who pioneered the foundation of statistics. The core concept of BDCP inherits Bayes’ scientific research, aiming to create a decentralized computing network protocol. Decentralization is the essence of the Web3.0 era, while computation refers to the calculation and processing of data through networks. BDCP is not only a computation protocol but also a supercomputing network system, comprising numerous supercomputers capable of handling large-scale computing tasks. The research team behind BDCP consists of professors and experts from the California Institute of Artificial Intelligence, who initiated discussions on decentralized machine learning in 2019 and eventually developed this protocol leveraging blockchain technology.

Network Layer: Decentralized Communication Infrastructure

The network layer of BDCP ensures efficient communication between various data centers and computing nodes within decentralized computing networks. Unlike traditional centralized networks, BDCP adopts a peer-to-peer (P2P) communication protocol, eliminating centralized control and ensuring the decentralized nature of the network. In the BDCP network, each data center internally connects numerous computers through local networks, forming computing clusters, while communication between multiple data centers is facilitated through P2P protocols. This design not only ensures high bandwidth and low-latency communication but also provides infinite scalability potential for supercomputing networks, enabling them to handle massive data and computing tasks.

Data Storage and Management Layer: Secure and Reliable Distributed Storage

In the BDCP protocol, the data storage and management layer aim to achieve data storage and management in a decentralized environment. The storage process involves data organization, encryption, multiple backups, and data validation steps. Initially, users organize and encrypt the data to ensure that only those with the keys can decrypt and access it. Subsequently, the data management protocol of BDCP searches for data centers in the decentralized network that meet the storage requirements, storing and backing up the data on multiple nodes to prevent data loss. In case a data center goes offline, the protocol automatically restores data backups, ensuring data integrity and security. Additionally, data storage paths and backup information are recorded for users to retrieve and recover data at any time.

Machine Learning Layer: Distributed Learning Breaking Data Silos

The distributed machine learning layer of BDCP addresses the challenge of processing massive data by assigning large-scale data learning tasks to multiple computing nodes. Traditional machine learning typically relies on single or a few computers, whereas BDCP utilizes a decentralized supercomputing network to divide learning tasks into subtasks distributed among different computing nodes for processing. After each node completes its assigned tasks, the results are returned to the master program to generate a comprehensive machine learning model.

BDCP’s distributed machine learning not only enhances computational efficiency but also resolves the issue of data silos. Data centers of different enterprises (such as Amazon and Walmart) can engage in collaborative learning through the BDCP protocol without sharing raw data, thus generating more comprehensive and accurate recommendation system models. This distributed learning mode not only protects data privacy but also enhances model generalization, enabling better service across various application scenarios.

Intelligent Application Layer: Supporting Multi-Domain Intelligent Application Development

The intelligent application layer is an integral part of the BDCP protocol, supporting the development and operation of various intelligent applications based on artificial intelligence technology. BDCP provides robust computational power and abundant computing resources, supporting application development in multiple technology domains such as machine learning, natural language processing, and image processing. For example, through machine learning technology, BDCP can build personalized recommendation systems to enhance user experience; natural language processing technology can be used for automatic translation and sentiment analysis, aiding enterprises in better understanding user needs; image processing technology finds extensive applications in fields like facial recognition and autonomous driving.

The intelligent application layer of BDCP not only offers robust computational support but also allows enterprises and developers to leverage the advantages of decentralized networks in developing and deploying efficient and secure intelligent applications. This decentralized intelligent application development model will significantly promote the application and popularization of artificial intelligence technology across various industries.

Value Transformation Layer: Realizing the Value of Data and Intelligent Applications

The value transformation layer is the highest level of the BDCP protocol, aiming to realize the value of data and intelligent applications. In a decentralized network, after individual and enterprise data are encrypted and protected, they can participate in machine learning and intelligent applications, receiving corresponding rewards for data and model contributions. Through BDCP’s distributed machine learning, data silos are broken, generating more comprehensive intelligent models that can serve different enterprises and application scenarios, maximizing the value of data.

In the value transformation layer of BDCP, individual data can participate in machine learning by authorization, gaining corresponding rewards; enterprise data, through collaborative learning, generates higher-quality models, enhancing business efficiency and competitiveness. Additionally, investors and miners of the decentralized network protocol and the Bayesian blockchain project will also receive corresponding rewards, achieving true value sharing. This value transformation mechanism not only improves the utilization of data and intelligent applications but also provides powerful momentum for the development of decentralized computing and supercomputing networks.


The launch of the BDCP protocol mainnet signifies a new era for decentralized computing and supercomputing. Through the collaboration of the network layer, data storage management layer, machine learning layer, intelligent application layer, and value transformation layer, BDCP not only provides a powerful computing platform but also paves the way for the realization of data value. In the future, BDCP will continue to drive the development of decentralized computing, laying a solid foundation for the arrival of the Web3.0 era. The BDCP protocol not only addresses the challenges of data storage and computation but also pioneers a new model of data value transformation through distributed machine learning and intelligent applications, truly realizing the decentralized utilization and value sharing of data.


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Disclaimer: This press release may contain forward-looking statements. Forward-looking statements describe future expectations, plans, results, or strategies (including product offerings, regulatory plans and business plans) and may change without notice. You are cautioned that such statements are subject to a multitude of risks and uncertainties that could cause future circumstances, events, or results to differ materially from those projected in the forward-looking statements, including the risks that actual results may differ materially from those projected in the forward-looking statements.

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