5 citations · 5 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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 (…
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…
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…
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…