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.