354 citations · 1.4k across the 34 of their papers we have counts for
35 papers
Understanding Communication Backends in Cross-Silo Federated Learning
Amir Ziashahabi, Chaoyang He, Salman Avestimehr
Federated learning (FL) has emerged as a practical means for privacy-preserving distributed machine learning. FL's versatile design makes it suitable for various training settings,…
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is…
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations…
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learnin…
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
Jiaqi Shao, Shanshan Han, Chaoyang He +1
Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as c…
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
Shanshan Han, Baturalp Buyukates, Zijian Hu +13
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defens…
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran +7
Federated Learning (FL) enables collaborations among clients for train machine learning models while protecting their data privacy. Existing FL simulation platforms that are design…
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Weizhao Jin, Yuhang Yao, Shanshan Han +5
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated…
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Baturalp Buyukates, Chaoyang He, Shanshan Han +5
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to…
Federated Analytics: A survey
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han +4
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g.,…
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing
Chaoyang He, Shuai Zheng, Aston Zhang +4
The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost. MoE selects different sets of parameters (i.e., expe…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
Sunwoo Lee, Anit Kumar Sahu, Chaoyang He +1
Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple w…
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Erum Mushtaq, Chaoyang He, Jie Ding +1
Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulat…
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Xinle Wu, Dalin Zhang, Chenjuan Guo +3
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solu…
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang +7
Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer visio…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Chaoyang He, Zhengyu Yang, Erum Mushtaq +3
Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data pr…
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
Sunwoo Lee, Tuo Zhang, Chaoyang He +1
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters. It is, however, known that different layers…
FairFed: Enabling Group Fairness in Federated Learning
Yahya H. Ezzeldin, Shen Yan, Chaoyang He +2
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as…
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
Jinhyun So, Chaoyang He, Chien-Sheng Yang +5
Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregati…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
Jiacheng Liang, Songze Li, Bochuan Cao +2
We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their…
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Chaoyang He, Emir Ceyani, Keshav Balasubramanian +2
Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-str…