5 citations · 9 across the 5 of their papers we have counts for
6 papers
Compressed LLM Reprogramming for Vision-Aided Beam Prediction in Vehicular Networks
Kai Dong, Lei Wang, Changyi Li +2
Large Language Model (LLM) reprogramming-based beam prediction demonstrates strong data efficiency by adapting pretrained language models for vehicle-to-infrastructure (V2I) beam p…
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
Ehsan Lari, Reza Arablouei, Stefan Werner
We propose a Byzantine-resilient federated conformal prediction (FCP) method that leverages partial model sharing, where only a subset of model parameters is exchanged each round.…
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Ehsan Lari, Reza Arablouei, Stefan Werner
We propose PRISM-FCP (Partial shaRing and robust calIbration with Statistical Margins for Federated Conformal Prediction), a communication-efficient Byzantine-robust federated conf…
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To ad…
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicio…
Analysis of Distributed Average Consensus Algorithms for Robust IoT networks
Sateeshkrishna Dhuli, Fouzul Atik
Internet of Things(IoT) is a heterogeneous network consists of various physical objects such as large number of sensors, actuators, RFID tags, smart devices, and servers connected…