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12/13/2022
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
Today, recommender systems have played an increasingly important role in...
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12/13/2022
AFLGuard: Byzantine-robust Asynchronous Federated Learning
Federated learning (FL) is an emerging machine learning paradigm, in whi...
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08/17/2022
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Federated learning (FL) has received a surge of interest in recent years...
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02/18/2021
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
A key challenge of big data analytics is how to collect a large volume o...
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01/27/2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Federated learning (FL) is a distributed machine learning architecture t...
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12/27/2020
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Byzantine-robust federated learning aims to enable a service provider to...
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02/19/2020
Toward Low-Cost and Stable Blockchain Networks
Envisioned to be the future of distributed systems, blockchain networks ...
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02/19/2020
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems
Recommender system is an essential component of web services to engage u...
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01/12/2020
Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
With rise of machine learning (ML) and the proliferation of smart mobile...
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11/26/2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
In federated learning, multiple client devices jointly learn a machine l...
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09/10/2019
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach
In this work, we consider the resilience of distributed algorithms based...
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09/11/2018
