Federated Learning Architecture
Last updated
Last updated
PrompTale AI’s Federated Learning (FL) framework enhances AI model performance through a decentralized, user-centric system that ensures privacy while enabling collaboration. The platform employs a Data-to-Earn model, which incentivizes users to contribute valuable, annotated data for training AI models without compromising privacy. By incorporating non-IID (Non-Independent and Identically Distributed) data, the system reflects real-world diversity, improving the robustness and adaptability of AI models. Data from users across various contexts and regions is integrated into the model to create a more comprehensive and versatile AI system.
ADDRESSING NON-IID DATA IN FEDERATED LEARNING
In traditional federated learning systems, data is often assumed to be IID (Independent and Identically Distributed), a condition that rarely holds in real-world applications. Non-IID data introduces significant challenges, such as bias and uneven model performance, as user data varies due to differences in behavior, environment, and other contextual factors. PrompTale AI handles non-IID data by utilizing a decentralized federated learning model that effectively aggregates diverse data from participants. This approach enables the model to adapt to different user patterns and environmental conditions, increasing the robustness of the AI.
FEDERATED LEARNING ARCHITECTURE
PrompTale AI’s federated learning system is designed to maintain privacy while promoting collaborative contributions to model training. The architecture consists of several key steps:
USER DATA PARTICIPATION: Users contribute annotated data to the system by selecting images and providing metadata, which represent a variety of concepts across different contexts. This data, originating from diverse sources, helps create a model that is not limited to a narrow dataset, thus improving the model’s generalization.
LOCAL DATA PROCESSING AND PARAMETER EXTRACTION: The federated model performs computations locally on users’ devices, ensuring that only the model parameters—not sensitive data—are shared with the central server. This guarantees privacy, as raw user data never leaves their device.
MODEL UPDATE AGGREGATION: Once the local updates are received, they are aggregated using a federated averaging algorithm. This ensures that the model learns from a wide range of user inputs, accommodating the inherent complexity of non-IID data distributions.
GLOBAL MODEL UPDATE: After aggregation, the global model is updated and sent back to the users. This iterative process allows the AI model to improve continuously, leveraging the diverse data contributed by users, while ensuring that the updates remain private and secure.
DECENTRALIZED HASPOWER AND PROOF OF VALIDATION
Incorporating Decentralized Hashpower and Proof of Validation (PoV) mechanisms further strengthens the federated learning system by promoting efficient and trustworthy collaboration across the network.
PrompTale's Decentralized Hashpower utilizes Virtual Processing Units (VPUs), allowing users to contribute computational resources in exchange for incentives. This decentralized compute model facilitates the scalable and cost-effective training of AI models, while ensuring that participants are rewarded for their contributions. The decentralized nature of the hashpower also reduces reliance on centralized infrastructures, addressing the cost and accessibility barriers associated with traditional AI systems.
Additionally, Proof of Validation (PoV) ensures that model updates are accurate and trustworthy. By incorporating a majority-based validation process, PoV mitigates risks such as model poisoning and malicious updates, ensuring that only high-quality data contributes to the model. Validators assess the quality of contributions before they are integrated into the global model, ensuring that the data driving the model is reliable, diverse, and non-biased.
Together, these mechanisms create a robust ecosystem where privacy, scalability, and trust are maintained, enabling effective collaboration in federated learning without compromising the security or quality of the AI model.
As Federated Learning becomes a core technology in the emerging Web3 ecosystem, PrompTale AI is at the forefront of integrating these innovations. By combining the Data-to-Earn model with Decentralized Hashpower and Proof of Validation, PrompTale AI facilitates a collaborative, transparent, and user-driven approach to AI development. These elements come together to create a more inclusive and efficient platform, enabling creators and users to directly contribute to the evolution of digital content and AI systems, while ensuring their privacy and incentivizing their participation.
