Mohamed Seif
University of Arizona
48 Papers
51 Citations
Mohamed Seif is an academic researcher from University of Arizona. The author has contributed to research in topics: Computer science & Channel state information. The author has an hindex of 9, co-authored 30 publications. Previous affiliations of Mohamed Seif include Nile University & Beni-Suef University.
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Papers
Wireless Federated Learning with Local Differential Privacy
Mohamed Seif,Ravi Tandon,Ming Li +2 more
- 21 Jun 2020
TL;DR: It is shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users, and a private wireless gradient aggregation scheme is proposed, which shows that when aggregating gradients from K users, the privacy leakage per user scales as O compared to orthogonal transmission.
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Bio-evaluation of crustacean and fungal nano-chitosan for applying as food ingredient.
TL;DR: In this article, two sources (shrimp wastes and fungus biomass) were used to produce chitosan and the chitin was extracted in the nano-form followed by characterization by transmission electron microscopy.
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Hepato-Renal protective Effects of Egyptian Purslane Extract against Experimental Cadmium Toxicity in Rats with Special Emphasis on the Functional and Histopathological Changes.
TL;DR: In this paper, a study was designed to clarify the hapato-nephroprotective effects of purslane ethanolic extract (PEE) against cadmium toxicity.
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Bio and phyto-chemical effect of Amphora coffeaeformis extract against hepatic injury induced by paracetamol in rats
TL;DR: Algal extract exhibited antagonistic effect against the hepatic injury and the deleterious effects induced by paracetamol in the extract simult-treated group and exhibited ameliorative effect againstThe electrophoretic alterations through restoring the absent normal bands and hiding the abnormal ones and hence increasing the SI values especially in the extracting group.
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Context-Aware Local Information Privacy
TL;DR: It is shown that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions, and some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge are presented.
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