Aggregation Strategy for Federated Machine Learning Algorithm
Rudolf Erdei, Daniela Delinschi, Iulia Băărăian, Oliviu Matei

Abstract. Federated learning allows multiple parties to collaboratively train a model without sharing their raw data, but the quality of the final model strongly depends on the aggregation strategy used to combine local updates. This work introduces an aggregation strategy designed for heterogeneous data sources typical of smart agriculture deployments, where clients differ in dataset size, data distribution and computational capacity. The proposed strategy adjusts the contribution of each client based on a combination of data quality and reliability indicators, while preserving privacy guarantees. Empirical evaluation shows improvements in model convergence and robustness compared to standard federated averaging baselines.

Keywords: federated learning; aggregation strategy; heterogeneous clients; smart agriculture; collaborative AI

Rudolf Erdei, Daniela Delinschi, Iulia Băărăian, Oliviu Matei, "Aggregation Strategy for Federated Machine Learning Algorithm", Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024, 2023.

Reference: Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024.

Other publications