Chaoyang He | DeepAI

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∙ 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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