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20212026
most citedDistFL: Distribution-aware Federated Learning for Mobile Scenarios

9 citations · 17 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.OS2025

Trustworthy and Controllable Professional Knowledge Utilization in Large Language Models with TEE-GPU Execution

Yifeng Cai, Zhida An, Yuhan Meng +5

Future improvements in large language model (LLM) services increasingly hinge on access to high-value professional knowledge rather than more generic web data. However, the data pr…

cs.CR2025

Who Grants the Agent Power? Defending Against Instruction Injection via Task-Centric Access Control

Yifeng Cai, Ziming Wang, Zhaomeng Deng +6

AI agents capable of GUI understanding and Model Context Protocol are increasingly deployed to automate mobile tasks. However, their reliance on over-privileged, static permissions…

cs.CR2025

Who Moved My Transaction? Uncovering Post-Transaction Auditability Vulnerabilities in Modern Super Apps

Junlin Liu, Zhaomeng Deng, Ziming Wang +6

Super apps are the cornerstones of modern digital life, embedding financial transactions into nearly every aspect of daily routine. The prevailing security paradigm for these platf…

cs.LG2025

Membership and Memorization in LLM Knowledge Distillation

Ziqi Zhang, Ali Shahin Shamsabadi, Hanxiao Lu +2

Recent advances in Knowledge Distillation (KD) aim to mitigate the high computational demands of Large Language Models (LLMs) by transferring knowledge from a large ''teacher'' to…

cs.CR2025

I Can Tell Your Secrets: Inferring Privacy Attributes from Mini-app Interaction History in Super-apps

Yifeng Cai, Ziqi Zhang, Mengyu Yao +8

Super-apps have emerged as comprehensive platforms integrating various mini-apps to provide diverse services. While super-apps offer convenience and enriched functionality, they ca…

cs.LG2025

Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models

Yifeng Cai, Ziqi Zhang, Ding Li +2

Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm tha…