Prediction Layer

Flex is a community-owned platform that rapidly boosts dApp user growth, ensuring the value generated directly benefits the Flex community.

The prediction layer uses machine learning models and time series analysis to forecast network activity and optimize resource allocation.

Task reward prediction model

A Long Short-Term Memory (LSTM) neural network is used to predict future task rewards:

JavaScriptCopy codefrom tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

def create_lstm_model(input_shape):
    model = Sequential([
        LSTM(64, input_shape=input_shape, return_sequences=True),
        LSTM(32, return_sequences=False),
        Dense(1)
    ])
    model.compile(optimizer='adam', loss='mse')
    return model

def predict_rewards(historical_data, model):
    # Preprocess data and make predictions
    pass

Network load balancing

This layer implements a dynamic load-balancing algorithm to optimize task distribution:

$$L(v)= T 1 ​ ∑ t∈T v ​ ​ P(v) C(t) ​$$

where:

• L(v) is the load factor of validator v

• Tv is the set of tasks assigned to validator v in the current time window

• T is the duration of the time window

• C(t) is the complexity of task t

• P(v) is the performance score of validator v

The network aims to minimize the variance of Lv across all validators.

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