Decentralized AI: how it works and the benefits
Artificial intelligence (AI) is traditionally based on centralized infrastructures. Today, a new approach is gaining momentum: decentralized AI. Decentralized AI relies on a system in which data processing and machine learning are performed across multiple locations rather than relying on a single central repository.This innovation promises to improve the security, privacy and efficiency of AI-enabled systems by eliminating the centralized checkpoints.
○ What is decentralized AI?
Decentralized artificial intelligence refers to an AI system where data processing and decision-making processes do not rely on a centralized infrastructure. Traditional AI is generally managed by centralized servers or clouds owned by a single entity. In contrast, decentralized AI relies on distributed technologies, such as blockchain or peer-to-peer (P2P) networks. This decentralization of AI enables the processing load to be shared between several nodes. This minimizes dependence on a single point of failure. Decentralized AI relies on Edge computing to process data locally on nearby devices or servers instead of remote data centers, and on federated learning to train models across multiple devices without sharing raw data. Decentralized AI via blockchain ensures responsible development and integration of AI models and agents. As a result, decentralized AI ensures greater data privacy, reduces latency and enables more personalized, real-time AI applications. This approach enhances security and resilience while eliminating single points of failure, thereby lowering the risk of large-scale data breaches.
The main goals of decentralized AI are to improve transparency, increase system resilience and offer users data control.
○ How does decentralized AI work?
Decentralized artificial intelligence is based on the use of distributed technologies such as blockchain. In fact, every node in a network can participate in data processing and decision-making without the intervention of a central authority. These technologies enable exchanges between nodes to be secured by consensus mechanisms. Decentralized AI also relies on edge computing to process data locally on nearby devices or servers instead of remote data centers, and on federated learning to train models on multiple devices without sharing raw data. We can take as an example, those used in crypto-currency systems (proof of work or proof of stake).
Here's an outline of how it works:
● Sharing the computing load: Data processing tasks are divided between several nodes, each contributing to the analysis and information management. This reduces the costs associated with managing centralized servers.
● Security and transparency through blockchain: Thanks to blockchain, every transaction or processing operation is recorded in an unalterable register, ensuring total traceability and enhanced security.
● Decentralized nodes : Unlike centralized systems where a single entity controls data and AI, decentralized AI distributes this management among several players, making the system less vulnerable to attacks and corruption.
For example, in decentralized neural networks, each node can contribute to part of the calculation, and the results are then aggregated to produce a final solution. This enables complex systems to operate autonomously, with enhanced security thanks to the data and computing distribution across local data centers such as those provided by UltraEdge.
○ What are the advantages of decentralized AI?
The benefits of decentralized AI extend beyond privacy concerns. Its power to revolutionize data analysis spreads across multiple societal sectors. Decentralized AI has several advantages over centralized AI systems:
■ Enhanced security
In a decentralized system, there is no single point of control. As a result, cyberattacks are harder to orchestrate. By distributing data processing across different nodes, the attack surface is drastically reduced. It can proactively detect anomalies or suspicious patterns in the data, acting as an effective early warning system against potential breaches. By distributing data processing across different nodes, the attack surface is significantly reduced, resulting in a significant increase in security.
■ Data confidentiality
One of the main assets of decentralized AI lies in the personal data management. Instead of centralizing sensitive information, each user or organization can retain direct control over their data. This therefore limits the risk of leakage or malicious exploitation linked to local data storage, and in compliance with standards such as the RGPD.
■ More resilient systems
Decentralized systems are more robust in the face of failure, so there is no single point of failure. If one node fails or is compromised, the other nodes continue to operate, ensuring continuity of service.
■ Reduced costs
By spreading the computing load across different nodes, companies can avoid the high costs associated with managing centralized data centers. This can also contribute to greater scalability, as resources can be adapted as needed and, in addition, there is no need for massive centralized power as in large data centers.
Today, all major pharmaceutical and biotechnology companies rely on data to drive innovation, facilitating new drug discovery and clinical trials. Decentralized AI enables them to improve their most critical processes by favoring secure data collaboration between organizations and preserving confidentiality.
○ What are the challenges and pitfalls of decentralized AI?
Despite its many advantages and huge potential, decentralized AI also faces several challenges.
■ Lack of standards
One of the biggest obstacles is the lack of standardization between inter-system formats. The technology is still relatively new (ChatGPT having been launched at the end of 2022), and there are as yet no widely adopted standards for the development and implementation of decentralized solutions. For decentralized AI to work effectively, data needs to be structured consistently and interoperably across different institutions and platforms. And that's to maximize its long-term potential!
■ Technical complexity
The implementation of decentralized AI systems requires advanced technical expertise and investment in infrastructure that can be costly, particularly in the blockchain and distributed network fields. This can be a barrier to adoption for some companies. Ensuring fair access to this technology is a key challenge. However, open source solutions are gradually emerging.
■ Scalability
Although decentralized AI allows tasks to be distributed across different nodes, it can sometimes encounter scalability issues, in particular with respect to coordination between nodes and resource management. A high concentration of AI resources and skills can throttle innovation and create barriers to entry for new players, which can limit the diversity of approaches and ideas.
Decentralized AI offers a promising new approach to improving the security, privacy, and efficiency of AI-based systems. Decentralizing AI has become a must to ensure the sustainable, ethical, and beneficial development of this transformative technology.
Although it remains in the development and adoption phase, its applications in sectors such as healthcare, finance and IoT are already demonstrating its potential.
For companies looking to explore this path, it is essential to prepare for the technical challenges while leveraging the many benefits of decentralization. The future of AI may well involve more open, secure and collaborative systems, with the adoption of Edge data centers as operated by shared hosting providers like UltraEdge.