20 citations · 27 across the 15 of their papers we have counts for
7 papers · 1 filter
Protecting Human Activity Signatures in Compressed IEEE 802.11 CSI Feedback
Mohamed Seif, Atsutse Kludze, Yasaman Ghasempour +3
Explicit channel state information (CSI) feedback in IEEE~802.11 conveys \emph{transmit beamforming directions} by reporting quantized Givens rotation and phase angles that paramet…
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
Liyan Xie, Muhammad Siddeek, Mohamed Seif +2
Watermarking has become a key technique for proprietary language models, enabling the distinction between AI-generated and human-written text. However, in many real-world scenarios…
Adversary-Aware Private Inference over Wireless Channels
Mohamed Seif, Malcolm Egan, Andrea J. Goldsmith +1
AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for vision and perception tasks such as…
Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency
Antti Koskela, Mohamed Seif, H. Vincent Poor +1
We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flipping with a random permutation of the…
Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission
Faranaksadat Solat, Joohyung Lee, Mohamed Seif +2
Hybrid Language Models (HLMs) combine the low-latency efficiency of Small Language Models (SLMs) on edge devices with the high accuracy of Large Language Models (LLMs) on centraliz…
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
Antti Koskela, Mohamed Seif, Andrea J. Goldsmith
We investigate privacy-preserving spectral clustering for community detection within stochastic block models (SBMs). Specifically, we focus on edge differential privacy (DP) and pr…
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?
Joohyung Lee, Jungchan Cho, Wonjun Lee +2
To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advant…