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

8 entries « ‹ 1 of 2 › »

Farcas, Allen-Jasmin; Song, Hyun Joon; Marculescu, Radu

Federated Continual Learning for Monocular Depth Estimation in Dynamic Indoor Environments Conference Forthcoming

21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), Forthcoming.

BibTeX

Farcas, Allen-Jasmin; Marculescu, Radu

Demo Abstract: Lightweight Training and Inference for Self-Supervised Depth Estimation on Edge Devices Conference Forthcoming

Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems (SenSys 2025), Forthcoming.

BibTeX

Farcas, Allen-Jasmin; Lee, Myungjin; Payani, Ali; Kompella, Ramana Rao; Latapie, Hugo; Marculescu, Radu

CHESSFL: Clustering Hierarchical Embeddings for Semi-Supervised Federated Learning Conference

Proceedings of the 9th ACM/IEEE Conference on Internet of Things Design and Implementation, 2024.

Links | BibTeX

Farcas, Allen-Jasmin; Marculescu, Radu

Demo Abstract: A Hardware Prototype Targeting Federated Learning with User Mobility and Device Heterogeneity Conference

International Conference on Internet-of-Things Design and Implementation (IoTDI), 2023.

Links | BibTeX

Farcas, Allen-Jasmin; Lee, Myungjin; Kompella, Ramana Rao; Latapie, Hugo; de Veciana, Gustavo; Marculescu, Radu

MOHAWK: Mobility and Heterogeneity-Aware Dynamic Community Selection for Hierarchical Federated Learning Conference

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023.

Links | BibTeX

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

Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Proceedings Article

In: Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (FedEdge), pp. 19-24, 2022.

Links | BibTeX

8 entries « ‹ 1 of 2 › »