1.Distributed Computation and Efficiency

Traditional fine-tuning depends heavily on centralized computational resources. Data and model updates must be transferred to and from a central server, which can be inefficient and time-consuming. Federated Learning, on the other hand, distributes both data and computational tasks. Each client performs training locally, and the central server's role is limited to aggregating the model parameters rather than handling raw data or performing heavy computations. This distributed approach not only improves efficiency but also reduces latency and the need for extensive data transfer.

2.Enhanced Privacy and Security

Traditional fine-tuning's reliance on centralized data poses significant privacy risks. By aggregating sensitive information in one place, the risk of data breaches and privacy violations increases. Federated Learning addresses this issue by decentralizing data ownership. In this approach, each client retains complete control over their own data. Training occurs locally on the client's device, and only model parameters are shared with a central server. This means that sensitive data never leaves the client's device, significantly enhancing data privacy and security.

···For example

Differential Privacy, DP: An algorithm M provides (δ,ϵ)-differential privacy protection on any adjacent datasets  D and D′ if for any output subset  Ω, the following holds：

Pr[M(D)∈Ω]≤eϵ⋅Pr[M(D')∈Ω]+δ

3.Scalability and Flexibility

TWith traditional fine-tuning, scaling the training process often requires significant infrastructure investments. Federated Learning inherently supports scalability by leveraging the computational power of distributed clients. As the number of clients increases, the system scales efficiently, without placing undue strain on a centralized server. This flexibility allows for faster and more scalable training processes, accommodating a wide range of AI models and applications.

4.Incentivization and Fairness

Traditional fine-tuning methods generally lack mechanisms for incentivizing contributors. Federated Learning platforms often integrate blockchain technology to create transparent and equitable reward systems. Contributors are recognized and rewarded for their participation and the quality of their updates, ensuring fair distribution of incentives and encouraging broader participation. This incentivization promotes a more collaborative and inclusive AI development environment.

1.Federated Learning Network

The motivation behind PoFL is to provide a new model of data ownership and sustainable incentivization/engagement for federated learning. Traditional centralized learning models often compromise data privacy and security. PoFL aims to decentralize the learning process and provide strong incentives for high-quality contributions. We anchor the user-end weight files on the blockchain and keep user data local. While protecting user privacy, this enhances the capabilities of our central model.

2.Construct PoFL Aggregate Score

We construct the POFL score based on the user's participation metrics and allocate rewards to participants in proportion to this score. ```

```

PoFL-Score_model = ∑ (γ × Improvement_global - δ × Degradation_global)

PoFL-Score_validation = ∑ (η × Correct_validation - θ × Incorrect_validation)

PoFL-Score = ∑ (Weight_stake × PoFL-Score_data) + PoFL-Score_model + PoFL-Score_validation

```

Participation metrics include but are not limited to the model contribution score and the verification accuracy score, with weights assigned to the number of file contributions.

3.Protocol Incentives

The protocol issues rewards in the form of tokens。 Rewards are distributed proportionally to the PoFL score, incentivizing high-quality data provision, impactful model contributions, and accurate validations. In the initial bootstrap phase, the focus is on increasing data volume and distribution. The protocol earns no revenue; users only pay for the minimal blockchain resources used to calculate and store their PoFL score and distribute associated incentives.

1.Latency 50ms (parameters)

A latency of 50ms for handling parameters, such as model weights or configuration settings, ensures that the system can quickly process and adjust these parameters. This low latency is vital for maintaining a seamless user experience and supporting efficient real-time processing, particularly in applications that require rapid response times, such as financial trading and real-time data analysis.

2. OnChain File 2KB

Limiting the on-chain file size to 2KB is essential for the scalability and cost-efficiency of a blockchain system. A smaller file size reduces the amount of data stored and executed on the blockchain, leading to lower storage costs, reduced bandwidth requirements, and faster transaction processing. This also translates to decreased computational resource usage and energy consumption, making the system more efficient and environmentally sustainable.

3. Training Energy Cost

Implementing high-parameter efficiency algorithms can reduce the time required for processing and adjusting model parameters. This includes utilizing low-latency communication protocols that adapt quickly to changes, ensuring rapid and efficient parameter updates. Furthermore, it can reduce the cost of model training by approximately 30% -50%, depending on the application scenario and the complexity of the training tasks.

4. Local efficiency improvement

By fine-tuning only a small portion of the parameters in a large model while keeping other parameters unchanged, the computing resources and video memory required for training are reduced. Training time may be reduced by 80% -90%, and video memory may be reduced by 50%-90%. Reduce storage requirements, further optimizing on-chain data management and ensuring the system remains lightweight and scalable.