[HTML][HTML] {FLAME}: Taming backdoors in federated learning
With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from
home, corporate Virtual Private Networks (VPNs) have become an important item securing …
home, corporate Virtual Private Networks (VPNs) have become an important item securing …
Membership Inference Attacks and Defenses in Federated Learning: A Survey
Federated learning is a decentralized machine learning approach where clients train
models locally and share model updates to develop a global model. This enables low …
models locally and share model updates to develop a global model. This enables low …
Not one less: Exploring interplay between user profiles and items in untargeted attacks against federated recommendation
Federated recommendation (FR) is a decentralised approach to training personalised
recommender systems, protecting users' privacy by avoiding data collection. Despite its …
recommender systems, protecting users' privacy by avoiding data collection. Despite its …
SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor Attacks
Federated Learning (FL), an emerging distributed machine learning framework that enables
each client to collaboratively train a global model by sharing local knowledge without …
each client to collaboratively train a global model by sharing local knowledge without …
Get rid of your trail: Remotely erasing backdoors in federated learning
Federated learning (FL) enables collaborative learning across multiple participants without
exposing sensitive personal data. However, the distributed nature of FL and unvetted …
exposing sensitive personal data. However, the distributed nature of FL and unvetted …
Federated learning: Challenges, SoTA, performance improvements and application domains
I Schoinas, A Triantafyllou, D Ioannidis… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …
enabling collaborative training of models in a distributed environment while ensuring privacy …
Depriving the Survival Space of Adversaries Against Poisoned Gradients in Federated Learning
Federated learning (FL) allows clients at the edge to learn a shared global model without
disclosing their private data. However, FL is susceptible to poisoning attacks, wherein an …
disclosing their private data. However, FL is susceptible to poisoning attacks, wherein an …
SkyMask: Attack-agnostic robust federated learning with fine-grained learnable masks
Federated Learning (FL) is becoming a popular paradigm for leveraging distributed data
and preserving data privacy. However, due to the distributed characteristic, FL systems are …
and preserving data privacy. However, due to the distributed characteristic, FL systems are …
[HTML][HTML] FLSAD: Defending Backdoor Attacks in Federated Learning via Self-Attention Distillation
L Chen, X Liu, A Wang, W Zhai, X Cheng - Symmetry, 2024 - mdpi.com
Federated Learning (FL), as a distributed machine learning framework, can effectively learn
symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible …
symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible …
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
Federated learning (FL) has been widely deployed to enable machine learning training on
sensitive data across distributed devices. However, the decentralized learning paradigm …
sensitive data across distributed devices. However, the decentralized learning paradigm …