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A survey of recent advances in edge-computing-powered artificial intelligence of things
Z Chang, S Liu, X **_Federated_Learning_Security_from_a_Defenders_Perspective_A_Unified_CVPR_2024_paper.pdf" data-clk="hl=uk&sa=T&oi=gga&ct=gga&cd=2&d=13011345634947742228&ei=Cme9Z5GXOpeY6rQPksnM8AE" data-clk-atid="FLrj_OqVkbQJ" target="_blank">[PDF] thecvf.com
Revam** Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
Federated Learning (FL) facilitates clients to collaborate on training a shared machine
learning model without exposing individual private data. Nonetheless FL remains …
learning model without exposing individual private data. Nonetheless FL remains …
Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL
is a recent distributed training scheme where multiple clients send intermediate activations …
is a recent distributed training scheme where multiple clients send intermediate activations …
Split learning with differential privacy for integrated terrestrial and non-terrestrial networks
Integrated terrestrial and non-terrestrial networks (TNTNs) have become a promising
architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical …
architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical …
Aegis: Mitigating targeted bit-flip attacks against deep neural networks
Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …
Rve-pfl: Robust variational encoder-based personalised federated learning against model inversion attacks
Federated learning (FL) enables distributed joint training of machine learning (ML) models
without the need to share local data. FL is, however, not immune to privacy threats such as …
without the need to share local data. FL is, however, not immune to privacy threats such as …
Actionbert: Leveraging user actions for semantic understanding of user interfaces
As mobile devices are becoming ubiquitous, regularly interacting with a variety of user
interfaces (UIs) is a common aspect of daily life for many people. To improve the …
interfaces (UIs) is a common aspect of daily life for many people. To improve the …
Towards practical secure neural network inference: the journey so far and the road ahead
Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
Anonymous and efficient authentication scheme for privacy-preserving distributed learning
Distributed learning is proposed as a promising technique to reduce heavy data
transmissions in centralized machine learning. By allowing the participants training the …
transmissions in centralized machine learning. By allowing the participants training the …