Overview
What this challenge is about.
Simulate two hospitals with non-IID data using Flower, run FedAvg for 50 rounds, and write a security spec — earn a verifiable certificate.
The scenario
The Basel healthtech (around 22 staff, post-seed, focused on cardiology AI) operates in a regulatory environment (Swiss + EU GDPR) where federated learning is the only path to cross-institution training.
The Brief
What you'll do, and what you'll demonstrate.
Prototype a federated-learning setup across two simulated hospital sites with documented privacy properties and a technical spec for IT review.
Earning criteria — what you'll demonstrate
- Implement FedAvg with non-IID client data
- Compare federated vs centralized training fairly
- Apply secure aggregation and document its threat model
- Communicate the limits of federated learning to a security audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Federated Learning
Apply federated learning to solve real industry problems and demonstrate production-level capability.
- Fedavg
Apply fedavg to solve real industry problems and demonstrate production-level capability.
- Secure Aggregation
Apply secure aggregation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Flower
Apply flower to solve real industry problems and demonstrate production-level capability.
- Distributed Systems
Apply distributed systems to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Federated-learning prototyping is the applied research work that healthtech and cross-institutional AI teams need on roadmap.
This challenge sharpens
- federated-learning
- fedavg
- distributed-systems
AI Safety Researcher
Documenting the threat model around federated learning and secure aggregation is exactly the AI safety research work that regulated AI teams need.
This challenge sharpens
- federated-learning
- secure-aggregation
- fedavg
AI Solutions Architect
Writing the technical spec that hospital IT signs off on is core AI solutions architecture work in healthcare and regulated industries.
This challenge sharpens
- federated-learning
- distributed-systems
- secure-aggregation