A Federated Learning Framework for Resource Constrained Fog Networks
Wassila Lalouani, Mohamed Younis · 2022 IEEE Symposium on Computers and Communications (ISCC) · 2022
Federated learning (FL) is a collaborative framework that aggregates multiple machine learning (ML) models and thus enables efficient handling of data collected from numerous and diverse IoT devices. However, traditional FL frameworks often do not consider the connectivity and the heterogeneity of the data sources and hence suffer accuracy imbalance among the individual (local) ML models and in turn, negatively impact the aggregated model. To mitigate such a shortcoming, this paper promotes a two-step optimization process. The first focuses on data offloading from devices to fog nodes with the objective of improving the accuracy of local ML models under resource and connectivity constraints. The second provides statistical distribution aware data offloading that trades off the communication cost and the accuracy of local ML models. We validate the advantages of our approach using a benchmark dataset.