1 citations · 2 across the 6 of their papers we have counts for
6 papers
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels
Satyavrat Wagle, Seyyedali Hosseinalipour, Naji Khosravan +1
Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL has traditionally been studied for supervised ML tasks, in many applications, it is im…
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal +4
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely ne…
RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Minghui Liwang, Seyyedali Hosseinalipour +3
Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic natu…
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange
Satyavrat Wagle, Seyyedali Hosseinalipour, Naji Khosravan +2
Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for…
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio +4
Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a metho…
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Junghoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum +3
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We…