Privacy Protection Mechanisms of Federated Learning in Financial Risk Control

Federated learning is a distributed machine learning technology whose core objective is to collaboratively train a model with multiple participants (such as banks and financial institutions) without directly sharing raw data. In financial risk control, federated learning protects privacy through the following mechanisms:

1. Local Training and Model Updates
   Each participant (e.g., a bank) trains the model locally using its own data, generating model parameters (such as gradients or weights) without uploading raw data. This ensures that sensitive data (e.g., user transaction records) always remains local.

2. Secure Aggregation
   Participants upload their locally trained, encrypted model parameters to a central server. The server aggregates these parameters using secure aggregation algorithms (e.g., based on homomorphic encryption or secure multi-party computation) to generate a global model. During this process, the server cannot decrypt any single participant's parameters and can only obtain the aggregated result.

3. Differential Privacy Injection
   To prevent the inference of raw data from model parameters, participants add noise (e.g., Gaussian or Laplace noise) to the parameters before uploading, meeting differential privacy requirements. The intensity of the noise is controlled by a privacy budget (ε), balancing privacy protection and model accuracy.

4. Model Distribution and Iteration
   The server distributes the aggregated global model to each participant, who further fine-tunes it using local data. After multiple iterations, the model gradually converges, ultimately forming a high-performance global risk control model.

Example Illustration:
Assume Bank A and Bank B need to jointly train an anti-fraud model but cannot share user data. Both parties train the model using their local data and only upload the model weights. The server aggregates the weights and returns the new model to both parties. Throughout the process, data never leaves the local environment, and parameters are encrypted and noised, effectively preventing privacy leaks.