Search Results for author:
Found 10 papers, 2 papers with code
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants.
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE.
Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems
The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms.
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter.
An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation
A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed.
Cascading Bandit under Differential Privacy
This paper studies \emph{differential privacy (DP)} and \emph{local differential privacy (LDP)} in cascading bandits.
Conservative Contextual Combinatorial Cascading Bandit
We propose the novel \emph{conservative contextual combinatorial cascading bandit ($C^4$-bandit)}, a cascading online learning game which incorporates the conservative mechanism.
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
On Geometric Structure of Activation Spaces in Neural Networks
Based on this new algorithm, four common geometric properties shared by the activation spaces are concluded, which gives a rather clear description of the activation spaces.
