5 citations · 5 across the 7 of their papers we have counts for
13 papers
Structuring and Merging Deletion Demands on Crowdsourced Channel Knowledge Maps
Zihao Ding, Jun Huang
A base station can build a channel knowledge map (CKM) from the uplink pilots of its terminals and sweep only the beams that the map ranks highest. A fault at a terminal or at the…
Initiating Removal from Operational Symptoms in Federated Vehicular Networks
Zihao Ding, Jun Huang, Qiang Duan
Connected vehicles train a driving model through federated learning (FL) and drive with it, so a faulty contribution often surfaces only as a delayed symptom on the road. Federated…
Federated Unlearning of Quantum Data via Classical Shadows
Zihao Ding, Jun Huang, Cong-cong Xing
Quantum federated learning trains a quantum neural network (QNN) across nodes that hold quantum data, such as the states of quantum sensors. To forget data, unlearning methods for…
Tracing Knowledge Lineage for Federated Unlearning under Mutual Distillation
Zihao Ding, Jun Huang, Guodong Wang
An edge server and nearby devices can train language models of different sizes together by mutual distillation. Federated unlearning (FU) and unlearning for large language models (…
ERRAND: Budgeted Maintenance of Agent Memory
Beining Wu, Zihao Ding, Jun Huang
Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then moves while the store stand…
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
Zihao Ding, Jun Huang, Liang Dong
Embedded AI agents on edge devices can learn as a federation and keep improving after deployment. Each agent collects new trajectories with the current policy, and the edge server…
Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory
Beining Wu, Zihao Ding, Jun Huang +1
On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights. This memory is hard-bounded and exposed: it consumes RAM and…
SCALE: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing
Zihao Ding, Beining Wu, Jun Huang
Federated Unlearning (FU) is emerging as a powerful tool that enables the selective removal of client data to effectively address data contamination and meet strict privacy regulat…
PRISM: Exposing and Resolving Spurious Isolation in Federated Multimodal Continual Learning
Beining Wu, Zihao Ding, Jun Huang
While current federated multimodal continual learning over mixture-of-experts low-rank adaptation (MoE-LoRA) is built on the unverified assumption that routing isolates task-specif…
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
Zihao Ding, Beining Wu, Jun Huang
Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles…
Application-Aware Twin-in-the-Loop Planning for Federated Split Learning over Wireless Edge Networks
Zihao Ding, Beining Wu, Jun Huang +1
We investigate task-success-oriented resource allocation for federated split learning (FSL) at the wireless edge. In this setting, the server must jointly determine bandwidth, tran…
RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge
Beining Wu, Zihao Ding, Jun Huang
Federated learning (FL) over heterogeneous IoT edge devices faces coupled system-modality-data heterogeneity: the lower-cost device carries both fewer sensors and less computationa…
Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
Beining Wu, Zihao Ding, Leo Ostigaard +1
Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations…