This is a prototype for the software system explained in the paper: Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud Detection feel free to use this code for inspiration/Implement the monitoring in your environment.

This code contains an implementation of the Kolmogorov-Smirnov (KS) Test, designed explicitly for distributed edge computing environments (Edge-ML for instance). Our design ensures efficiency for both the distributed clients and the aggregating server.

Before running the framework, ensure you have the following prerequisites:

• Python 3.x
• Access to AWS SQS or a local stack setup for it
• Access to AWS lambda or a local stack setup for it (or just use the sqs_server as I did in the mock e2e simulation)
• Redis Instance

1. Install Dependencies: Refer to requirements.txt for the necessary Python packages and install them using pip install -r requirements.txt.

2. Setup cloud/local environment: The queue, the lambda, and the Redis instance

1. Configure AWS SQS/Local Stack: Update the client configuration framework/edge_client/client_configs.json and server configuration framework/queue_consumer_server/server_configs.json files with the details of your SQS setup.
2. Configure Redis: Update the Redis client configs in the SQSServer code.

1. Run the mock Simulation: Execute application_e2e_simulation.py to start an end-to-end mock of the system.
2. Edit the code to match your needs and environment...