activity
20152022
most citedWireless Federated Learning with Local Differential Privacy

25 citations · 34 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CR2022★ 4 cited

Answering Count Queries for Genomic Data with Perfect Privacy

Bo Jiang, Mohamed Seif, Ravi Tandon +1

In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect…

cs.SI2022★ 5 cited

Differentially Private Community Detection for Stochastic Block Models

Mohamed Seif, Dung Nguyen, Anil Vullikanti +1

The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by a…

cs.IT2021

Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

Mohamed Seif, Wei-Ting Chang, Ravi Tandon

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local di…

cs.CR2020★ 25 cited

Wireless Federated Learning with Local Differential Privacy

Mohamed Seif, Ravi Tandon, Ming Li

In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (…

cs.IT2018

Secure Retrospective Interference Alignment

Mohamed Seif, Ravi Tandon, Ming Li

In this paper, the -user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure ret…

cs.IT2016

Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms

Mohamed Seif, Tamer Elbatt, Karim G. Seddik

Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has…