127 citations · 461 across the 65 of their papers we have counts for
15 papers · 1 filter
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
Dong-Jun Han, Do-Yeon Kim, Minseok Choi +2
A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (…
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…
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…
Linear Coding for Gaussian Two-Way Channels
Junghoon Kim, Seyyedali Hosseinalipour, Taejoon Kim +2
We consider linear coding for Gaussian two-way channels (GTWCs), in which each user generates the transmit symbols by linearly encoding both its message and the past received symbo…
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…
AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework with Oblivious Smart Contracts
Servio Palacios, Aaron Ault, James V. Krogmeier +2
This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and rep…
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
Jing Guo, Raghu G. Raj, David J. Love +1
Signal classification problems arise in a wide variety of applications, and their demand is only expected to grow. In this paper, we focus on the wireless sensor network signal cla…
Deep Reinforcement 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 alter the wireless propagation environment through design of their reflection coefficients. We c…
Process-BERT: A Framework for Representation Learning on Educational Process Data
Alexander Scarlatos, Christopher Brinton, Andrew Lan
Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn.…
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton
A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL…
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
Bhargav Ganguly, Seyyedali Hosseinalipour, Kwang Taik Kim +4
We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at…
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
Dinh C. Nguyen, Seyyedali Hosseinalipour, David J. Love +2
In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile de…
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi +4
Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global agg…
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu +1
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank chara…