fair federated learning

**Fair Federated Learning** is a **federated learning approach that ensures equitable model performance across all participating clients** — preventing the scenario where the global model performs well on average but poorly for certain clients with minority data distributions. **Fairness Approaches** - **AFL (Agnostic FL)**: Optimize the worst-case client loss — ensure no client is left behind. - **q-FFL**: Assign higher weight to clients with higher loss — focus on underperforming clients. - **FedMGDA+**: Multi-objective optimization — find Pareto-optimal solutions across all clients. - **Per-Client Thresholds**: Set minimum performance thresholds for each client. **Why It Matters** - **Equity**: Without fairness constraints, majority clients dominate — minority clients get poor models. - **Manufacturing**: A model that works for Tool A but not Tool B is unfair and operationally useless. - **Incentive**: Clients won't participate in FL if the resulting model doesn't perform well for them. **Fair FL** is **no client left behind** — ensuring the federated model performs well for every participant, not just on average.