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Found 20 papers, 2 papers with code
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
Existing defenses mainly focus on protecting the training phase of FL such that the learnt global model is poison free.
Byzantine-Robust Decentralized Federated Learning
However, due to its fully decentralized nature, DFL is highly vulnerable to poisoning attacks, where malicious clients could manipulate the system by sending carefully-crafted local models to their neighboring clients.
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
A popular approach to mitigate impacts of incomplete client participation is the server-assisted federated learning (SA-FL) framework, where the server is equipped with an auxiliary dataset.
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data.
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data.
GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
In this study, we propose GradSafe, which effectively detects jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs.
Poisoning Federated Recommender Systems with Fake Users
Current poisoning attacks on federated recommender systems often rely on additional information, such as the local training data of genuine users or item popularity.
Competitive Advantage Attacks to Decentralized Federated Learning
In SelfishAttack, a set of selfish clients aim to achieve competitive advantages over the remaining non-selfish ones, i. e., the final learnt local models of the selfish clients are more accurate than those of the non-selfish ones.
AFLGuard: Byzantine-robust Asynchronous Federated Learning
Asynchronous FL aims to address this challenge by enabling the server to update the model once any client's model update reaches it without waiting for other clients' model updates.
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset.
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question.
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.
