Data Summarization for Federated Learning - Réseaux, Informatique, Systèmes de Confiance Access content directly
Conference Papers Year : 2023

Data Summarization for Federated Learning


We explore data summarization techniques as a mean to reduce the energy footprint of Federated Learning (FL). We formulate the problem of selecting a small subset of data points that best represent the gradient of each local dataset as a submodular maximization problem and provide sufficient conditions under which the FL training is guaranteed to converge to the same global model as if the whole local datasets have been used on each client. Experimental results on IID and non-IID datasets show that this approach yields a similar accuracy as training on the full local datasets, but with a significant reduction of runtimes. There is however no clear advantage of data summarization over random sampling.
Fichier principal
Vignette du fichier
MLNreport.pdf (1.17 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04295982 , version 1 (20-11-2023)


  • HAL Id : hal-04295982 , version 1


Julianna Devillers, Olivier Brun, Balakrishna Prabhu. Data Summarization for Federated Learning. Proceedings of the 6th International Conference on Machine Learning for Networking (MLN'2023), Nov 2023, Paris, France. ⟨hal-04295982⟩
85 View
17 Download


Gmail Facebook X LinkedIn More