How does Flock reshape AI production relations?

Author: LindaBell

In the last round of decentralized AI craze, star projects such as Bittensor, io.net and Olas quickly became industry leaders with their innovative technologies and forward-looking layout. However, as the valuations of these old projects continue to rise, the threshold for ordinary investors to participate is also getting higher and higher. So, in the face of this round of sector rotation, are there any new opportunities for participation?

Flock: Decentralized AI Training and Verification Network

Flock is a decentralized AI model training and application platform that combines federated learning and blockchain technology to provide users with a secure model training and management environment while protecting data privacy and fair community participation. The word Flock first came into the public eye in 2022, when its founding team jointly published an academic paper titled "FLock: Defending malicious behaviors in federated learning with blockchain", proposing the idea of ​​introducing blockchain into federated learning to prevent malicious behavior. The paper explains how to strengthen data security and privacy protection in the model training process through decentralized mechanisms, and also reveals the application potential of this new architecture in distributed computing.

After the initial proof of concept, Flock launched the decentralized multi-agent AI network Flock Research in 2023. In Flock Reseach, each agent is a large language model (LLM) tuned for a specific field, which can provide users with insights in different fields through collaboration. Then in mid-May 2024, Flock officially opened the testnet of the decentralized AI training platform, where users can participate in the training and fine-tuning of the model by using the test token FML and receive rewards. As of September 30, 2024, the number of daily active AI engineers on the Flock platform has exceeded 300, and the cumulative number of submitted models has reached more than 15,000.

With the continuous development of the project, Flock has also attracted the attention of the capital market. In March this year, Flock completed a $6 million financing led by Lightspeed Faction and Tagus Capital, with participation from DCG, OKX Ventures, Inception Capital and Volt Capital. It is worth noting that Flock is also the only AI infrastructure project that received funding in the 2024 Ethereum Foundation Academic Funding Round.

Reshaping the cornerstone of AI production relations: Introducing smart contracts for federated learning

Federated learning (Federated Learning Federated Learning is a machine learning method that allows multiple entities (usually called clients) to jointly train models while ensuring that data is stored locally. Unlike traditional machine learning, federated learning avoids uploading all data to a central server, but protects user privacy through local computing. At present, federated learning has actually been applied in many practical scenarios. For example, Google has introduced federated learning into its Gboard input method since 2017 to optimize input suggestions and text predictions while ensuring that user input data is not uploaded. Tesla also uses similar technology in its autonomous driving system to improve the vehicle's environmental perception capabilities in a local way and reduce the need for massive video data transmission.

However, these applications still have some problems, especially in terms of privacy and security. First, users need to trust centralized third parties. Secondly, in the process of model parameter transmission and aggregation, it is also necessary to prevent malicious nodes from uploading false data or malicious parameters, which will cause the model to deviate from the overall performance or even output incorrect prediction results. According to the research published by the FLock team in the IEEE journal, the accuracy of the traditional federated learning model will drop to 96.3% when 10% of malicious nodes exist, and when the proportion of malicious nodes increases to 30% and 40%, the accuracy drops to 80.1% and 70.9% respectively.

To solve these problems, Flock introduced smart contracts on the blockchain as a "trust engine" in its federated learning architecture.  As a trust engine, smart contracts can realize automated parameter collection and verification in a decentralized environment, and publish model results unbiasedly, thereby effectively preventing malicious nodes from tampering with data. Compared with traditional federated learning solutions, FLock's model accuracy can still be maintained above 95.5% even when 40% of the nodes are malicious nodes.

Positioning the AI ​​execution layer, analyzing the three-layer architecture of FLock

A major pain point in the current AI field is that the resources for AI model training and data use are still highly concentrated in the hands of a few large companies, and it is difficult for ordinary developers and users to use these resources effectively. Therefore, users can only use pre-built standardized models, and cannot customize them according to their own needs. This mismatch between supply and demand also means that even if the market has abundant computing power and data reserves, it cannot be converted into practical models and applications.

To address this problem, Flock hopes to become an effective scheduling system for coordinating demand, resources, computing power and data. Flock draws on the Web3 technology stack to position itself as the "execution layer", because as a core function, it is mainly responsible for allocating users' customized AI needs to various decentralized nodes for training, and scheduling these tasks to run on nodes around the world through smart contracts.

At the same time, in order to ensure the fairness and efficiency of the entire ecosystem, the FLock system is also responsible for "settlement" and "consensus". Settlement refers to the incentives and management of the contributions of participants, and rewards and penalties based on the completion of tasks. Consensus is responsible for evaluating and optimizing the quality of training results to ensure that the final generated model can represent the global optimal solution.

The overall product architecture of FLock consists of three major modules: AI Arena, FL Alliance and AI Marketplace. Among them, AI Arena is responsible for decentralized model basic training, FL Alliance is responsible for model fine-tuning under the smart contract mechanism, and AI Marketplace is the final model application market.

AI Arena: Localized Model Training and Verification Incentives

AI Arena is Flock's decentralized AI training platform. Users can participate by staking Flock testnet tokens FML and receive corresponding staking rewards. After the user defines the required model and submits the task, the training node in AI Arena will use the given initial model architecture to train the model locally without uploading the data directly to the centralized server. After each node completes the training, a validator will be responsible for evaluating the work of the training node, checking the quality of the model and scoring it. If you do not want to participate in the verification process, you can also choose to delegate your tokens to the validator to receive rewards.

In AI Arena, the reward mechanism for all roles depends on two core factors: the number of stakes and the quality of the task. The number of stakes represents the "commitment" of the participant, while the quality of the task measures its contribution value. For example, the rewards for training nodes depend on the number of stakes and the quality ranking of the submitted model, while the rewards for validators depend on the consistency of voting results with consensus, the number of staked tokens, and the number of times and successes of participating in validation. The benefits of delegators depend on the validators they choose and the number of stakes.