Federated Learning

Large amounts of data are generated nowadays on edge devices, such as phones, tablets, and wearable devices. However, since data on such personal devices is highly sensitive, training ML models by sending the users’ local data to a centralized server clearly involves significant privacy risks. Hence, in order to enable intelligence for these privacy-critical applications, Federated Learning (FL) has become the de facto paradigm for training ML models on local devices without sending data to the cloud. Our research in this direction focuses on developing new FL approaches that exploit data and devices heterogeneity.

federated learning

Selected Publications

Farcas, Allen-Jasmin; Chen, Xiaohan; Wang, Zhangyang; Marculescu, Radu

Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Inproceedings Forthcoming

In: FedEdge 2022 - 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network, Forthcoming.

BibTeX

Chen, Wei; Bhardwaj, Kartikeya; Marculescu, Radu

FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning Journal Article

In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) , 2020.

Links | BibTeX

Lin, Ching-Yi; Marculescu, Radu

Model Personalization for Human Activity Recognition Inproceedings

In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1–7, IEEE 2020.

Links | BibTeX