🤝Welcome To TuskNet
Your Data, Your Control. Collective Intelligence, Powered by Walrus.
Say Hi to TuskNet! A decentralized platform built on the Walrus network, designed to enable secure and privacy-preserving model training across distributed datasets. It ensures that sensitive data remains confidential by leveraging advanced cryptographic methods, including Pedersen commitments, to safeguard model parameters (weights and biases) throughout the training process.
The Walrus network serves as the backbone for decentralised coordination, facilitating secure participation of nodes in training and enabling on-chain aggregation of encrypted data.
Train Smarter. Collaborate Freer.
Data is the lifeblood of AI. But who controls the veins?
In a world where algorithms hunger for your most personal insights, we're rewriting the rules of engagement. Your data, your sovereignty.
AI's next frontier isn't just intelligence—it's trust.
Imagine training cutting-edge models without surrendering your most intimate data points. Federated learning isn't just a technology; it's a promise. A promise that collaboration can coexist with complete privacy.
Key Features
BlockChain Coordination (Walrus)
TuskNet leverages Walrus' power to manage node registration, emit real-time events, and securely aggregate encrypted data—all seamlessly and without compromise. It ensures every contributor can participate in the process, confident that their efforts are secure and valued.
Privacy-Preserving Model Training
We live in a world where data privacy is no longer optional—it is a necessity. TuskNet embraces this by employing Pedersen commitments, a cutting-edge cryptographic method that ensures your data and model parameters remain confidential.
Decentralized Node Participation
In the past, access to advanced AI development was reserved for the few—those with vast resources and centralized control. TuskNet flips this paradigm on its head. Now, anyone with data can participate as a node in a trustless ecosystem.
Encrypted Aggregation
With TuskNet, nodes submit encrypted model updates—weights and biases—that are aggregated on-chain by smart contracts on the Walrus network. The result? A seamless integration of contributions, enabling better AI models without ever exposing private data.
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