178 citations · 1.1k across the 57 of their papers we have counts for
63 papers
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…
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…
Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks
Rohit Parasnis, Seyyedali Hosseinalipour, Yun-Wei Chu +2
Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communi…
DAG-based Task Orchestration for Edge Computing
Xiang Li, Mustafa Abdallah, Shikhar Suryavansh +2
As we increase the number of personal computing devices that we carry (mobile devices, tablets, e-readers, and laptops) and these come equipped with increasing resources, there is…
Interference Cancellation GAN Framework for Dynamic Channels
Hung T. Nguyen, Steven Bottone, Kwang Taik Kim +2
Symbol detection is a fundamental and challenging problem in modern communication systems, e.g., multiuser multiple-input multiple-output (MIMO) setting. Iterative Soft Interferenc…
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…