217 citations · 463 across the 13 of their papers we have counts for
14 papers
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Haibo Yang, Peiwen Qiu, Prashant Khanduri +2
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been obse…
Competitive Advantage Attacks to Decentralized Federated Learning
Yuqi Jia, Minghong Fang, Neil Zhenqiang Gong
Decentralized federated learning (DFL) enables clients (e.g., hospitals and banks) to jointly train machine learning models without a central orchestration server. In each global t…
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
Minghong Fang, Jia Liu, Michinari Momma +1
Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems be…
AFLGuard: Byzantine-robust Asynchronous Federated Learning
Minghong Fang, Jia Liu, Neil Zhenqiang Gong +1
Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly learn a model with the help of a cloud server. A fundamental challenge of FL is that the…
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Xin Zhang, Minghong Fang, Zhuqing Liu +3
Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. A…
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
Minghong Fang, Minghao Sun, Qi Li +3
A key challenge of big data analytics is how to collect a large volume of (labeled) data. Crowdsourcing aims to address this challenge via aggregating and estimating high-quality d…
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, Jia Liu
Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received in…
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu +1
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byz…
Toward Low-Cost and Stable Blockchain Networks
Minghong Fang, Jia Liu
Envisioned to be the future of secured distributed systems, blockchain networks have received increasing attention from both the industry and academia in recent years. However, blo…
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems
Minghong Fang, Neil Zhenqiang Gong, Jia Liu
Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdso…
Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
Xin Zhang, Minghong Fang, Jia Liu +1
With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In thi…
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia +1
In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master dev…
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach
Haibo Yang, Xin Zhang, Minghong Fang +1
In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who coul…
Poisoning Attacks to Graph-Based Recommender Systems
Minghong Fang, Guolei Yang, Neil Zhenqiang Gong +1
Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnera…