Chaoyang He | DeepAI

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Yang Liu
344 publications
Ming Li
199 publications
Mehdi Bennis
180 publications
Bin Yang
145 publications
Li Shen
141 publications
Ji Liu
135 publications
Kai Zhang
131 publications
Dawn Song
130 publications
Xiang Ren
120 publications
Junzhou Huang
106 publications
Martin Jaggi
104 publications

∙ 07/05/2023

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data

Federated heavy-hitter analytics involves the identification of the most...

0 Jiaqi Shao, et al. ∙

∙ 07/02/2023

Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

Deep learning models are prone to forgetting information learned in the ...

0 Sara Babakniya, et al. ∙

∙ 06/08/2023

FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs

This paper introduces FedMLSecurity, a benchmark that simulates adversar...

7 Shanshan Han, et al. ∙

∙ 03/20/2023

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

Federated Learning (FL) enables machine learning model training on distr...

2 Weizhao Jin, et al. ∙

∙ 03/03/2023

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

Federated Learning (FL) enables collaborations among clients for train m...

2 Zhenheng Tang, et al. ∙

∙ 02/27/2023

Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain

We consider a project (model) owner that would like to train a model by ...

11 Baturalp Buyukates, et al. ∙

∙ 12/10/2022

SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing

The mixture of Expert (MoE) parallelism is a recent advancement that sca...

12 Chaoyang He, et al. ∙

∙ 01/11/2022

Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits

Local Stochastic Gradient Descent (SGD) with periodic model averaging (F...

0 Sunwoo Lee, et al. ∙

∙ 12/27/2021

SPIDER: Searching Personalized Neural Architecture for Federated Learning

Federated learning (FL) is an efficient learning framework that assists ...

10 Erum Mushtaq, et al. ∙

∙ 12/21/2021

AutoCTS: Automated Correlated Time Series Forecasting – Extended Version

Correlated time series (CTS) forecasting plays an essential role in many...

12 Xinle Wu, et al. ∙

∙ 11/22/2021

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

Federated Learning (FL) is a distributed learning paradigm that can lear...

2 Chaoyang He, et al. ∙

∙ 10/19/2021

Layer-wise Adaptive Model Aggregation for Scalable Federated Learning

In Federated Learning, a common approach for aggregating local models ac...

16 Sunwoo Lee, et al. ∙

∙ 10/06/2021

SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision

Federated Learning (FL) is transforming the ML training ecosystem from a...

6 Chaoyang He, et al. ∙

∙ 10/02/2021

FairFed: Enabling Group Fairness in Federated Learning

As machine learning becomes increasingly incorporated in crucial decisio...

5 Yahya H. Ezzeldin, et al. ∙

∙ 09/29/2021

LightSecAgg: Rethinking Secure Aggregation in Federated Learning

Secure model aggregation is a key component of federated learning (FL) t...

0 Chien-Sheng Yang, et al. ∙

∙ 06/15/2021

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

Federated learning can be a promising solution for enabling IoT cybersec...

10 Tuo Zhang, et al. ∙

∙ 06/04/2021

SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks

Graph Neural Networks (GNNs) are the first choice methods for graph mach...

4 Chaoyang He, et al. ∙

∙ 05/09/2021

Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search

Single Image Super-Resolution (SISR) tasks have achieved significant per...

11 Han Huang, et al. ∙

∙ 04/14/2021

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

Graph Neural Network (GNN) research is rapidly growing thanks to the cap...

54 Chaoyang He, et al. ∙

∙ 02/05/2021

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers

The size of Transformer models is growing at an unprecedented pace. It h...

29 Chaoyang He, et al. ∙

∙ 07/28/2020

Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge

Scaling up the convolutional neural network (CNN) size (e.g., width, dep...

0 Chaoyang He, et al. ∙

∙ 04/18/2020

FedNAS: Federated Deep Learning via Neural Architecture Search

Federated Learning (FL) has been proved to be an effective learning fram...

24 Chaoyang He, et al. ∙

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

∙ 10/11/2019

Central Server Free Federated Learning over Single-sided Trust Social Networks

Federated learning has become increasingly important for modern machine ...

0 Chaoyang He, et al. ∙

∙ 06/27/2019

Efficient Spatial Anti-Aliasing Rendering for Line Joins on Vector Maps

The spatial anti-aliasing technique for line joins (intersections of the...

1 Chaoyang He, et al. ∙

∙ 06/27/2019

Adversarial Representation Learning on Large-Scale Bipartite Graphs

Graph representation on large-scale bipartite graphs is central for a va...

3 Chaoyang He, et al. ∙

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