1 citations · 2 across the 10 of their papers we have counts for
10 papers
Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour +2
Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for…
Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
Su Wang, Mung Chiang, H. Vincent Poor
We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers. Wh…
Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
Su Wang, Mung Chiang, H. Vincent Poor
We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/fog devices contain separate da…
AoI-based Scheduling of Correlated Sources for Timely Inference
Md Kamran Chowdhury Shisher, Vishrant Tripathi, Mung Chiang +1
We investigate a real-time remote inference system where multiple correlated sources transmit observations over a communication channel to a receiver. The receiver utilizes these o…
Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels
Md Kamran Chowdhury Shisher, Vishrant Tripathi, Mung Chiang +1
The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper, we study optimal resource alloca…
Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour +2
Most federated learning (FL) approaches assume a fixed device set. However, real-world scenarios often involve devices dynamically joining or leaving the system, driven by, e.g., u…
Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover
Dong-Jun Han, Wenzhi Fang, Seyyedali Hosseinalipour +2
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality commu…
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
Dong-Jun Han, Seyyedali Hosseinalipour, David J. Love +2
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting a…
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang, Roberto Morabito, Seyyedali Hosseinalipour +2
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities
Roberto Morabito, Mallik Tatipamula, Sasu Tarkoma +1
The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages. These appl…