Hugging Face Trending Papers July 16, 2026

FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels

Read the original on Hugging Face Trending Papers →

Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer, where user payloads are encrypted and Radio Network Temporary Identifiers (RNTIs) may change over time.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

llms

arXiv AI

Jul 20

FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels

arXiv:2607. 15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage.

By Md Nahid Hasan Shuvo, Mahmudul Hassan Ashik, Moinul Hossain

llms

arXiv AI

23h ago

Learnware for CSI Feedback: Scene-specific Small Models Can Do Big

arXiv:2608. 17760v1 Announce Type: cross Abstract: Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems, yet existing deep learning solutions face a trade-off between model generalization and scenario-specific performance.

By Xiangyi Li, Jiajia Guo, Chao-Kai Wen, Xin Geng, Shi Jin, Zhi-Hua Zhou

rag fine-tuning safety

arXiv AI

Jun 9

Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices

arXiv:2606. 07857v1 Announce Type: cross Abstract: The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preservation and real-time responsiveness.

By Stefan Behfar, Richard Mortier

llms robotics fine-tuning safety

arXiv Machine Learning

Jul 9

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.

By Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti, Danny Yuxing Huang

llms robotics efficiency safety

arXiv Machine Learning

1d ago

6G Native AI and Channel Foundation Models

arXiv:2608. 14591v1 Announce Type: cross Abstract: The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems.

By Shugong Xu, Jun Jiang, Yuan Gao

llms

arXiv AI

Jul 14

JEPA for AI-Native 6G: Predictive Representations and Open Challenges

arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.

By Sheikh Salman Hassan, Irshad A. Meer, Almoatssimbillah Saifaldawla, Yan Kyaw Tun, Mustafa Ozger, Madyan Alsenwi, Nguyen Van Huynh, Woong-Hee Lee, Cedomir Stefanovic, Mathini Sellathurai, Henk Wymeersch, Tharmalingam Ratnarajah

rag diffusion benchmarks

Related stories

FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels

Learnware for CSI Feedback: Scene-specific Small Models Can Do Big

Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

6G Native AI and Channel Foundation Models

JEPA for AI-Native 6G: Predictive Representations and Open Challenges