# A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions Source: [A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions]() Collection index: [llms.txt]() Data updated: 2026-05-14T05:00:05.130Z Authors: Antonios Makris, Christos Dousis, Emmanouil Kritharakis, Stavros Bouras, Konstantinos Tserpes Published: 2026-05-10T11:04:30.000Z ## Abstract Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by the aggregation strategy used to combine local model updates at the server, which directly affects learning performance, robustness, and system behavior. This work presents a comprehensive experimental comparison of widely used federated aggregation strategies under both homogeneous and heterogeneous data distributions. Using benchmark image classification datasets, we analyze how different aggregation mechanisms respond to varying degrees of data heterogeneity, examining their impact on centralized accuracy and loss, and system-level efficiency metrics, including aggregation, training, and communication time. The results demonstrate that aggregation strategies exhibit distinct trade-offs across datasets and data distributions, with their effectiveness varying according to dataset characteristics and operating conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()