# 4. Federated Learning System

&#x20;

### Introduction

The Federated Learning System in the Devolved AI platform allows for decentralized AI model training across various devices and locations. By aggregating learning from multiple sources without sharing raw data, it enhances privacy and data security. This system supports collaborative AI advancements, enabling diverse datasets to contribute to model accuracy and robustness.

### Key Principles

**Decentralized Training Infrastructure**

Decentralized training infrastructure employs a network of distributed nodes, each contributing computational power for AI model training, enhancing resilience, and reducing bottlenecks.

Unlike conventional AI training setups that rely on centralized servers and data repositories, Devolved AI's Federated Learning System distributes the training process across a network of participant-owned GPUs. This decentralized infrastructure not only enhances security and privacy but also democratizes access to AI development.

**Community-Powered Training**

The heart of the Federated Learning System lies in community participation. Individuals with GPUs can actively contribute their computational resources to the training of AI models. This community-powered approach accelerates the training process, harnessing the collective strength of diverse computing setups. Similarly, data and inference feedback submitted by the community is a driving force for our AI models.

**Democratizing AI Development**

Devolved AI's commitment to inclusivity extends to its Federated Learning System. By enabling individuals worldwide to contribute to AI training, the platform democratizes AI development. Whether you're a GPU owner, an AI enthusiast, or a seasoned developer, everyone has a role in shaping the future of AI.

### Benefits of Federated Learning

**Speed and Efficiency**

Harnessing the combined computational power of distributed GPUs, the platform achieves faster convergence and more efficient training cycles.

**Privacy-Preserving Collaboration**

The Federated Learning System inherently preserves privacy by allowing AI models to learn from decentralized data sources without needing to share or centralize sensitive data. This approach ensures that personal or confidential information remains within the local domain, significantly mitigating privacy risks.

**Global Network of GPUs**

As the community expands, the platform taps into an increasingly diverse range of computing resources, further enhancing the system's scalability and collective learning capacity.

**Community Engagement and Rewards**

Contributors to the Federated Learning System are not only pivotal to AI development but are also duly rewarded through the PoV protocol. Individuals providing GPU resources earn Argocoins (AGC) as a recognition of their valuable contribution.
