client selection strategies

**Client Selection Strategies** in federated learning determine **which subset of clients to participate in each training round** — smart selection based on data quality, diversity, resource availability, or contribution improves convergence speed, model quality, and fairness. **Selection Strategies** - **Random**: Uniformly random selection — simple, unbiased, but ignores client heterogeneity. - **Power of Choice**: Select clients with higher local loss — focus on clients where the model performs worst. - **Clustered Selection**: Select clients from diverse clusters to maximize data diversity per round. - **Resource-Aware**: Prioritize clients with sufficient compute and connectivity. **Why It Matters** - **Convergence Speed**: Smart selection can reduce the number of communication rounds by 2-5×. - **Fairness**: Random selection may consistently under-represent minority clients — active selection ensures coverage. - **System Efficiency**: Selecting clients likely to finish on time avoids straggler delays. **Client Selection** is **choosing the right participants** — strategically selecting clients each round to maximize learning efficiency and model quality.