# Architecture and Contribution Guide

- Language: Python 3.8+
- Core Tech Stack: NumPy for numeric computations; a minimal federated learning abstraction.
- Key Components:
  - Client: local dataset, performs simple gradient descent to update weights, caches updates when offline.
  - Server: aggregates client weight deltas with optional DP-noise, updates global model.
- Testing:
  - Run tests via ./test.sh which executes pytest and validates packaging with python -m build.
- How to contribute:
  - Implement new privacy-preserving aggregation strategies or richer client models.
  - Extend tests to cover offline caching and connectivity scenarios.