Home > Published Issues > 2022 > Volume 13, No. 6, December 2022 >
JAIT 2022 Vol.13(6): 539-548
doi: 10.12720/jait.13.6.539-548

An Adaptive Model Averaging Procedure for Federated Learning (AdaFed)

Alessandro Giuseppi 1, Lucrezia Della Torre 1, Danilo Menegatti 1, Francesco Delli Priscoli 1, Antonio Pietrabissa1, and Cecilia Poli 2
1. University of Rome La Sapienza, Rome, Italy
2. Istituto Superiore di Sanità, Rome, Italy

Abstract—Federated Learning (FL) is an enabling technology for Machine Learning in scenarios in which it is impossible, for privacy and/or regulatory reasons, to analyze data in a centralized manner. FL envisages that distributed clients cooperate to learn a model without any data exchange, in favor of a model averaging procedure that is coordinated by a server. In this work, we present the Adaptive Federated Learning (AdaFed) algorithm, that extends the original Federated Averaging algorithm by: (i) dynamically weighting the local models, based on their performance, for the averaging procedure; (ii) adapting the loss function at every communication round depending on the training behavior. This work specializes AdaFed for both classification and regression tasks, and reports several validation tests on benchmarking dataset, showing its enhanced robustness against unbalanced data distributions and adversarial clients. 
Index Terms—federated learning, distributed learning systems, adaptive learning, deep neural networks 

Cite: Alessandro Giuseppi, Lucrezia Della Torre, Danilo Menegatti, Francesco Delli Priscoli, Antonio Pietrabissa, and Cecilia Poli, "An Adaptive Model Averaging Procedure for Federated Learning (AdaFed)," Journal of Advances in Information Technology, Vol. 13, No. 6, pp. 539-548, December 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.