How FLock is democratising AI development
5 min readMar 3, 2024

Collaboration is the bedrock of innovation. It is by pooling our collective expertise that we can reach groundbreaking solutions at lightning speed.

As it stands, there is an overbearing barrier to this in the AI world: low public participation, lack of privacy, and the tight grip of a select few corporations controlling how AI is developed and hosted.

FLock’s community-driven, privacy-centric approach is here to change that. In this article, we explain how FLock is democratising AI creation.

The problem with centralised control over AI creation

As it stands, hindering innovation is the centralised control over AI. In other words, control, decision-making, and data storage are concentrated in a single authority or location.

Firstly, this means that closed-source large language model (LLM) providers like OpenAI can monitor all user interactions with the model, compromising privacy.

Since centralised institutions entirely control the ChatGPT model, there is no governance over the model’s output. In recent cases, we have seen dramatic amplification of model biases and inaccuracies.

Finally, every user of the centralised LLM is essentially an accessible data contributor to such large corporations who own the model. The fairness of contribution incentives and data value assessments must be improved.

FLock is democratising AI creation

Move over, big tech — FLock’s collaboration platform is empowering the general public to roll up their sleeves. Now, anyone can contribute knowledge and enrich AI models themselves.

The result? Community-owned models built by the many, not just the few. FLock’s library ranges from intelligent agents to sophisticated trading and confluence bots, pioneering a new era of accessible AI tools.

FLock’s vision is to democratise AI model training, fine-tuning and inferencing, empowering, while preserving privacy.

FLock’s key features

Community-owned models:

The platform enables the community to actively participate in expanding and refining knowledge bases for AI models, ensuring a diverse and rich input source while maintaining strict data privacy standards.

Privacy-preserved data:

FLock deploys cutting-edge technologies such as federated learning, zkFL, homomorphic encryption, and Secure Multi-Party Computation (SMPC) to protect user data privacy. Data remains safely on the user’s device, preventing central data collection and potential misuse.

Decentralised compute:

FLock leverages decentralised computing power to train and fine-tune AI models. This approach enables scalability, reduces costs, and enhances security.

FLock products and use cases

Co-creation Platform:

Our co-creation platform enables the community to actively participate in expanding and refining knowledge bases for AI models.

A prime example is BTC-GPT, which achieved 5,000 calls in just three weeks. This ensures a diverse and rich input source while maintaining strict data privacy standards.

Federated Learning Client:

Federated learning is a method of training models where the training data remains decentralised on individual devices or servers, instead of sending it to a central server.

This approach to model training upholds our commitment to data privacy by allowing data to remain on the user’s device, preventing central data collection and potential misuse.

This innovative technology has been recognised for its excellence, winning an award at the prestigious NeurIPS conference and highlighting our leadership in creating impactful, privacy-preserving AI solutions.

A series of use cases already serve users:

a. AI-enabled FVM: Leveraging machine learning to enhance file management and operations.

b. Crowdsourced 0–1 Code Auditing: Utilising the power of the crowd to improve code quality and security, emphasising our belief in community-driven development.

c. Decentralised Health Tracking & Alerts: Offering a privacy-centric health monitoring solution reflects our commitment to secure and user-focused applications.

d. DID & Credit Score: Pioneering in decentralised identity and credit scoring, illustrating our dedication to revolutionising personal finance through Web3 technologies.

Curious about FLock’s role in the AI ecosystem?

The AI ecosystem is transitioning from Web2’s centralised services (AWS) to Web3’s decentralised networks (such as Render Network and I/O Net), representing a significant leap towards a more inclusive computing ecosystem.

Initiatives like Bittensor have demonstrated the practicality of decentralised resource scheduling. Centralised platforms (such as OpenAI) have streamlined the data processing and model training processes, and now, with FLock technology, communities are incentivised to contribute their idle resources with privacy assurance.

We categorise the Web2 AI and Web3 AI ecosystems into different layers, with positioned at the foundational layer of the Web3 AI ecosystem. It fosters decentralised data contribution by incentivising it, coordinates computing power within a decentralised framework for model training and fine-tuning, supports on-chain federated learning, and provides solutions for multiple stages of Web3 AI, thereby enhancing the entire ecosystem.

FLock’s local models are trained on decentralised training nodes and synchronised with the global model. The nature of Federated Learning ensures user data is kept safely locally. FLock also employs technologies like zkFL, homomorphic encryption, and Secure Multi-Party Computation (SMPC) to provide extra protection for data privacy. The governance of the whole training process is achieved by blockchain, which is automated, transparent, and immutable.

Additionally, FLock supports various model structures, from complex neural networks to simpler statistical models. Through these aspects, FLock enables decentralised machine learning, thus addressing a series of issues caused by centralised control over AI models.

In the future, FLock will be the provider of ML models in Web3 and continue to improve them through user contributions and feedback. It is modular and can be plugged into any decentralised hosting network (, Gensyn, Ritual, etc.) to make its models more accessible and to any Dapps to improve their performance.

Find out more

FLock’s innovation is achieved through the fundamental research domain that we created in combination with federated learning and blockchain (check out our papers here).

If you’d like to get involved early with our product, please visit our Beta at: Users can accumulate points there.

For an overview of the Web3 AI ecosystem, revisit our previous article:

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