Review on Aggregation Frameworks For High-Performance Federated Learning

Abstract

Federated Learning (FL) enables collaborative model training across distributed clients while keeping their original data within local environments, thereby reducing the need for centralized data collection. Despite this advantage, the effectiveness of FL strongly depends on model aggregation, which determines how locally trained updates contribute to the global model. Conventional aggregation methods, particularly uniform or data-size-based averaging, may become ineffective when clients exhibit non-IID data distributions, class imbalance, unequal participation, device heterogeneity, client drift, variable update quality, and unreliable behaviour. This paper presents a systematic investigation of model aggregation strategies and organizes existing approaches into synchronous, asynchronous, hierarchical, adaptive, personalized, and robust aggregation frameworks. Their operating principles, advantages, limitations, and suitability for heterogeneous federated environments are comparatively examined. Particular emphasis is placed on aggregation mechanisms that account for local model performance, training loss, convergence behaviour, data contribution, statistical heterogeneity, and update quality when determining the influence of participating clients. The analysis reveals that fixed aggregation rules cannot adequately accommodate dynamically changing client and data characteristics. Accordingly, this work establishes the research direction toward an adaptive weighted aggregation framework in which client contributions are dynamically adjusted using multiple performance-related indicators. Such an approach is expected to improve global accuracy, convergence stability, fairness, minority-class representation, and robustness to low-quality updates, while retaining the privacy-preserving characteristics of FL. The study provides a foundation for developing reliable and scalable aggregation mechanisms for distributed applications including healthcare, intelligent transportation, smart cities, industrial systems, IoT, and next-generation communication networks.