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.