Yi Zhou

Overview

Title

Research Staff Member, Master Inventor

Location

IBM Research - Almaden San Jose, CA USA

Bio

Yi Zhou is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Her primary research interests lie in the design of efficient algorithms and techniques for problems that arise in the field of machine learning, federated learning, adversarial machine learning and data security and privacy, etc. More specifically, she worked on building IBM Federated Learning Library and defending against data poisoning attacks for the past few years. Recently, she is also interested in unlearning and data engineering for large language models.

Yi Zhou received her Ph.D. degree from Georgia Institute of Technology in 2018. Her doctoral dissertation focused on designing stochastic algorithms for distributed optimization and machine learning.

Publications

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
- - Syed Zawad
  - Ahsan Ali
  - et al.
- 2021
- AAAI 2021
Addressing Unique Fairness Obstacles within Federated Learning
- - Annie Abay
  - Ebube Chuba
  - et al.
- 2021
- AAAI 2021
Adaptive Histogram-based Gradient Boosted Trees For Federated Learning
- - Yuya Jeremy Ong
  - Yi Zhou
  - et al.
- 2020
- INFORMS 2020
TiFL: A Tier-based Federated Learning System
- - Zheng Chai
  - Ahsan Ali
  - et al.
- 2020
- HPDC 2020
A unified variance-reduced accelerated gradient method for convex optimization
- - Guanghui Lan
  - Zhize Li
  - et al.
- 2019
- NeurIPS 2019
A hybrid approach to privacy-preserving federated learning
- - Stacey Truex
  - Thomas Steinke
  - et al.
- 2019
- AISec/CCS 2019
Hybridalpha: An efficient approach for privacy-preserving federated learning
- - Runhua Xu
  - Nathalie Baracaldo
  - et al.
- 2019
- AISec/CCS 2019
A Hybrid Approach to Privacy-Preserving Federated Learning: (Extended Abstract)
- - Stacey Truex
  - Nathalie Baracaldo
  - et al.
- 2019
- Informatik-Spektrum
Towards taming the resource and data heterogeneity in federated learning
- - Zheng Chai
  - Hannan Fayyaz
  - et al.
- 2019
- OpML 2019

Visit Google Scholar

Patents

- 28 Oct 2024
- US
- 12131231
Federated Learning Technique For Applied Machine Learning
- 16 Sep 2024
- US
- 12093837
Building A Federated Learning Framework
- 01 Apr 2024
- US
- 11948096
Adaptively Adjusting Influence In Federated Learning Model Updates
- 11 Sep 2023
- US
- 11755954
Scheduled Federated Learning For Enhanced Search
- 19 Jun 2023
- US
- 11681951
Semantic Learning In A Federated Learning System
- 08 May 2023
- US
- 11645582
Parameter Sharing In Federated Learning
- 06 Mar 2023
- US
- 11601468
Detection Of An Adversarial Backdoor Attack On A Trained Model At Inference Time
- 20 Feb 2023
- US
- 11588621
Efficient Private Vertical Federated Learning
- 07 Nov 2022
- US
- 11494700
Semantic Learning In A Federated Learning System

Projects

Federated Systems

View more projects

Top collaborators

Yi Zhou

Overview

Title

Location

Bio

Publications

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

Addressing Unique Fairness Obstacles within Federated Learning

Adaptive Histogram-based Gradient Boosted Trees For Federated Learning

TiFL: A Tier-based Federated Learning System

A unified variance-reduced accelerated gradient method for convex optimization

A hybrid approach to privacy-preserving federated learning

Hybridalpha: An efficient approach for privacy-preserving federated learning

A Hybrid Approach to Privacy-Preserving Federated Learning: (Extended Abstract)

Towards taming the resource and data heterogeneity in federated learning

Patents

Federated Learning Technique For Applied Machine Learning

Building A Federated Learning Framework

Adaptively Adjusting Influence In Federated Learning Model Updates

Scheduled Federated Learning For Enhanced Search

Semantic Learning In A Federated Learning System

Parameter Sharing In Federated Learning

Detection Of An Adversarial Backdoor Attack On A Trained Model At Inference Time

Efficient Private Vertical Federated Learning

Semantic Learning In A Federated Learning System

Projects

Federated Systems

Top collaborators

Nathalie Baracaldo

Parikshit Ram

Heiko Ludwig

Swanand Ravindra Kadhe