9 citations · 16 across the 3 of their papers we have counts for
6 papers · 1 filter
Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton
Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in additio…
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim +2
A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust perf…
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio +4
Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student…
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…
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle +1
One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D…
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
Zhan-Lun Chang, Seyyedali Hosseinalipour, Mung Chiang +1
Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, wi…