4 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…
RateCount: Learning-Free Device Counting by Wi-Fi Probe Listening
Tianlang He, Zhangyu Chang, Zhongming Lin +1
Counting Wi-Fi devices within access point (AP) coverage by listening to their probe request frames (PRFs) is a well-established research problem, fundamental to many Internet of T…
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…
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…