most citedRobust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

quant-ph2023

DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms

Junyao Zhang, Hanrui Wang, Gokul Subramanian Ravi +4

This paper proposes DISQ to craft a stable landscape for VQA training and tackle the noise drift challenge. DISQ adopts a "drift detector" with a reference circuit to identify and…

cs.CR2023

PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information

Lin Duan, Jingwei Sun, Yiran Chen +1

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preservin…

cs.CL2023

Towards Building the Federated GPT: Federated Instruction Tuning

Jianyi Zhang, Saeed Vahidian, Martin Kuo +6

While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amou…

cs.LG2023

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Jingwei Sun, Ziyue Xu, Dong Yang +6

Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) dea…

cs.CR20233 cited

Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

Jingwei Sun, Zhixu Du, Anna Dai +4

Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to im…