20 papers
Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
Hanlin Cai, Kai Li, Houtianfu Wang +4
Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distr…
Low-Complexity Run-Length-Limited ISI-Mitigation (RLIM) Codes for Molecular Communication
Melih Åahin, Ozgur B. Akan
Molecular communication suffers from severe inter-symbol interference, which makes constrained coding essential for reliable transmission. Run-length-limited ISI-mitigation codes a…
Dispersion-Domain Detection for Mobile Molecular Communication Under Multiplicative Geometry Uncertainty
Shaojie Zhang, Ozgur B. Akan
Mobile molecular communication (MC) links with counting receivers are sensitive to transmitter--receiver geometry especially when nodes are mobile. We study binary detection from w…
A Control-Referenced Tri-Channel OECT Receiver for Hybrid Molecular Communication Toward Brain Organoid Interfaces
Hongbin Ni, Ozgur B. Akan
Brain organoid interfaces that seek neuromodulator readout benefit from chemical receivers with molecular specificity and tolerance to drift. This paper presents a receiver-centric…
Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents
Hanlin Cai, Houtianfu Wang, Haofan Dong +3
Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this pa…
Local Differential Privacy for Molecular Communication Networks
Melih Åahin, Ozgur B. Akan
Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate l…