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Rui M Zhang, 54910 Hillsleigh Rd, Alpharetta, GA 30022

Known as:
Rui C Zhang Rui T Zhang Etal Rui Zhang Mark R Zhang Rui Z Hang Zhang Rui
Related to:
Paul Zai Zhang Tianyi Dong Zongyu Zhang 48 Ho C Chang 64

Rui Zhang Phones & Addresses

Alpharetta, GA   

1521 Brompton Ct, Atlanta, GA 30338   

Dunwoody, GA   

Kennesaw, GA   

Berkeley, CA   

Decatur, GA   

Stone Mountain, GA   

Conyers, GA   

Stockbridge, GA   

5533 Mount Vernon Way, Atlanta, GA 30338   

Work

Position: Clerical/White Collar

Education

School / High School: Cornell Law School

Ranks

Licence: New York - Currently registered Date: 2012
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Mentions for Rui M Zhang

Career records & work history

Lawyers & Attorneys

Rui Zhang Photo 1

Rui Zhang - Lawyer

Address:
Citic Group Corporation
010-5966129 #6 (Office)
Licenses:
New York - Currently registered 2012
Education:
Cornell Law School
Rui Zhang Photo 2

Rui Zhang - Lawyer

ISLN:
924297020
Admitted:
2012

Medicine Doctors

Rui Zhang

Specialties:
Physical Medicine & Rehabilitation
Work:
Kessler Institute For RehabKessler Institute For Rehabilitation
1199 Pleasant Vly Way FL 1, West Orange, NJ 07052
973-7313600 (phone) 973-2436861 (fax)
Site
Languages:
English, Spanish
Description:
Dr. Zhang works in West Orange, NJ and specializes in Physical Medicine & Rehabilitation. Dr. Zhang is affiliated with Kessler Institute For Rehabilitation.
Rui Zhang Photo 3

Rui Zhang

Rui Zhang Photo 4

Rui Zhang

License Records

Rui Zhang

Licenses:
License #: 2347 - Active
Category: Landscape Architect
Issued Date: Jul 6, 2006
Expiration Date: Jul 31, 2017

Rui Zhang

Licenses:
License #: E093578 - Expired
Category: Emergency medical services
Issued Date: Dec 9, 2012
Expiration Date: Aug 31, 2014
Type: Los Angeles County EMS Agency

Publications & IP owners

Us Patents

Private And Federated Learning

US Patent:
2021040, Dec 30, 2021
Filed:
Sep 13, 2021
Appl. No.:
17/472843
Inventors:
- Armonk NY, US
Stacey Truex - Atlanta GA, US
Heiko H. Ludwig - San Francisco CA, US
Ali Anwar - San Jose CA, US
Thomas Steinke - Mountain View CA, US
Rui Zhang - San Francisco CA, US
International Classification:
H04L 9/08
H04L 9/00
G06F 21/62
G06N 20/20
G06F 16/25
G06F 16/2458
G06K 9/62
Abstract:
Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.

Private And Federated Learning

US Patent:
2020035, Nov 12, 2020
Filed:
May 7, 2019
Appl. No.:
16/405066
Inventors:
- Armonk NY, US
Stacey Truex - Atlanta GA, US
Heiko H. Ludwig - San Francisco CA, US
Ali Anwar - San Jose CA, US
Thomas Steinke - Mountain View CA, US
Rui Zhang - San Francisco CA, US
International Classification:
H04L 9/08
H04L 9/00
G06F 21/62
G06K 9/62
G06F 16/25
G06F 16/2458
G06N 20/20
Abstract:
Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.

Possible Relatives

David Zhang  Dawei Zhang  Dazhi Zhang  De K Zhang  Da Q Zhang  Dachuan Zhang  Daijun Zhang  Rui M Mendonca  Rui F Leal  Rui Li 

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