🧐 FLock Edge Compute New Narrative | Full Analysis of Bybit's Latest Launchpool Project FLock @flock_io——

Bybit has become one of the hottest cryptocurrency trading platforms, thanks to its focus on the Japanese and Korean markets and its rapid listing style;

Especially in the past six months, the efficiency of listings and the logic behind token selection have created a strong wealth effect, making Launchpool almost an Alpha mining machine;

What is the project FLock @flock_io launching this time——

Launchpool Staking Period:

Dec 31, 2024, 12 PM UTC – Jan 7, 2025, 12 PM UTC

1️⃣ What is FLock——

FLock is an innovative project supported by investments from DCG, Lightspeed Faction, Volt, Tagus, OKX Ventures, etc., focusing on providing decentralized privacy protection solutions for artificial intelligence.

Its core concept is to utilize blockchain technology through federated learning blocks (FLocks), allowing data holders to collaborate in machine learning without disclosing personal data. FLock offers modular computing power, data, and training methods, enabling decentralized development of AI models.

FLock aims to democratize AI through blockchain technology, allowing users to gain insights from their data while maintaining data privacy. Through a decentralized approach, FLock hopes to reduce reliance on central data collection entities, allowing users to decide how to monetize their data.

2️⃣ FLock's Technical Framework——

FLock focuses on decentralized training, employing federated learning to ensure that training data is learned by the model while maintaining decentralized storage that does not leak data privacy, directing training needs to decentralized computing platforms like Akash and http://IO.net;

This technology is referred to as a decentralized tech stack: FLock allows AI models to be trained while data remains local.

On another level, FLock emphasizes computation on edge devices rather than in the cloud. This strategy not only protects user privacy but also reduces latency and improves computational efficiency, especially in scenarios requiring immediate responses.

This method not only protects user data privacy but also enables efficient model training in a distributed environment.

3️⃣ FLock Edge Compute Narrative——

FLock believes that how to utilize private data while protecting the privacy of data providers is a key issue that needs to be addressed in the current AI field——

FLock Edge Compute is part of the FLock project, focusing on computation and AI model training and inference on edge devices. It combines edge computing and federated learning, providing a decentralized computing framework through blockchain technology.

The benefit of this approach is that training tasks can be pushed down to user terminals—such as smartphones, tablets, and laptops (edge devices).

Since the computation and learning processes occur on local devices, user data does not leave their devices, thereby enhancing data privacy protection. This method avoids the potential privacy risks associated with centralized data storage and processing in traditional cloud computing.

Thus, it can fully utilize the idle computing power of edge devices while ensuring data confidentiality.

With the support of this distributed computing power, models can obtain encrypted model parameters from various devices for integration, avoiding the leakage of raw data. This approach significantly reduces training latency, allowing models to quickly adapt to dynamic environments and provide precise support for niche areas.

The narrative of FLock Edge Compute is an attempt to challenge traditional AI development models, striving for broader applications of AI through technological innovation while ensuring privacy, security, and fairness.

3️⃣ Current Application Scenarios of FLock——

Currently, the FLock project has the potential to find applications in the following areas:

1) Healthcare:

Using federated learning, hospitals or research institutions can collaboratively develop health prediction models or disease diagnosis tools without sharing personal patient data.

For example:

University College London Hospital (blood sugar prediction algorithm)

Moorfields Eye Hospital (ophthalmology detection algorithm)

2) Financial Services:

Financial institutions can use FLock to train fraud detection models, relying on distributed data to improve model accuracy while protecting sensitive customer information.

For example, Request finance (on-chain credit scoring) can implement on-chain Sesame credit scores using FLock;

3) Internet of Things (IoT):

In smart homes, industrial IoT, and other fields, devices can utilize edge computing for real-time data analysis and decision-making, with FLock ensuring that this data processing occurs while protecting privacy.

In addition, FLock currently provides one-click deployment templates for training and validating models on Akash, where Akash community members can mine FLock and contribute templates using Akash computing. http://IO.net uses PoAI to provide node consensus and train anime image generation models. Aptos: trained a programming assistant for developers using the Move language for Aptos.

Conclusion——

The FLock project presents a vision with revolutionary potential for the future of AI development, especially in terms of privacy protection and decentralized computing.

Bybit excels at quickly identifying market demands and hotspots, and choosing FLock @flock_io surely has its merits in certain aspects;

We can pay attention to:

1) Bybit's Launchpool to acquire tokens;

2) Keep an eye on the FLock TGE timing for opportunities to participate;

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