Intro

A solution for the demand for a trustless decentralized GPU network. Orbitalx is a response to a growing demand for trustless AI solutions that respect privacy, leverage decentralized power, and align with the principles of Web3. Orbitalx is a decentralized network that harnesses the power of distributed GPU networks to train AI models in a way that keeps your data private and secure. We’re focused on making AI training scalable, efficient, and, most importantly, privacy-preserving. By tapping into a global network of idle GPUs, Orbitalx offers a new way to develop AI that stays true to the ethos of decentralization and privacy. Orbitalx is built on a decentralized network of nodes that contribute their GPU power to the platform. These nodes are the distributed, privacy-first system designed to train AI models without compromising user data. The USP of Orbitalx is how it handles AI training while keeping data safe & private.

Features:

Federated Learning - Instead of sucking up all data into a central server, Orbitalx lets AI models train directly on your device. The model learns from your data locally, and only the insights (not the actual data) get sent back to the network. This aggregated knowledge helps improve the global model without compromising individual privacy. Secure Multi-Party Computation (MPC) - Sometimes, AI training requires collaboration between multiple nodes. MPC makes this possible by allowing nodes to work together without sharing their actual data. Each node encrypts its data, performs computations, and only the final, combined result is used for model training. Zero-Knowledge Proofs (ZKPs) - To add another layer of security, we use ZKPs. These proofs allow the network to verify that nodes have done their work correctly, without needing to see the data they’ve used. It’s like checking homework without actually looking at the answers.

Main Sub-systems:

Nodes - Each node is equipped with GPU resources and operates independently, handling data locally. This means your data stays on your machine, and only the essential training outputs get shared with the network. Privacy Protocols - To keep everything secure and private, Orbitalx uses advanced cryptographic techniques like Secure Multi-Party Computation (MPC) and Federated Learning. These ensure that while nodes collaborate to train models, no raw data is ever exposed. Orchestration Layer - This layer smartly distributes AI training tasks across the network, making sure that each node gets work that matches its capabilities. Consensus Mechanism - We use something we call Proof-of-Training (PoT). In simple terms, nodes earn rewards for their GPU contributions, but only if the training work they produce meets the network’s standards. This keeps everyone honest and the models accurate.

The Orbitalx Ecosystem Runs on $ORB Tokens:

Rewards - If you’re contributing GPU power to Orbitalx, you’ll earn $ORB tokens. The more you contribute and the better your results, the more tokens you earn. It’s a straightforward way to incentivize participation. Staking for Security - To keep the network secure, nodes are required to stake $ORB tokens before they can join in on the action. This staking process ensures that nodes have something to lose if they act maliciously, which helps maintain the integrity of the network. Transaction Fees - When users want to train their AI models on Orbitalx, they’ll pay in $ORB tokens. These fees go back to the nodes that did the heavy lifting, creating a self-sustaining system where everyone benefits.