The authors compare different aggregation strategies in federated learning (how you combine model updates from distributed clients) under both homogeneous and heterogeneous data distributions. Using benchmark image classification datasets, they measure how strategies like FedAvg, FedProx, and others perform in terms of centralized accuracy, loss, and system-level metrics (aggregation time, training time, communication overhead). The results show that different aggregation methods have distinct trade-offs that depend on the dataset characteristics and degree of data heterogeneity, with no single winner across all conditions.
Main takeaways:
- Federated aggregation strategy (how server combines client updates) strongly affects both learning performance and system efficiency
- No single aggregation method dominates—trade-offs vary by dataset, data distribution (homogeneous vs. heterogeneous), and operating conditions
- System-level metrics matter: aggregation/training/communication time vary significantly across strategies
- Heterogeneous data distributions shift the relative ranking of aggregation methods compared to homogeneous (IID) settings