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Found 9 papers, 1 papers with code
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Our results also reveal that the local steps in FL could help the convergence and show that the maximum number of local steps can be improved to $T/m$ in full worker participation.
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model.
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems
Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem.
Toward Low-Cost and Stable Blockchain Networks
To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.
Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing.
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Our empirical results on four real-world datasets show that our attacks can substantially increase the error rates of the models learnt by the federated learning methods that were claimed to be robust against Byzantine failures of some client devices.
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 on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process.
Poisoning Attacks to Graph-Based Recommender Systems
To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users.
