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Found 10 papers, 2 papers with code
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP.
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry.
Features Disentangled Semantic Broadcast Communication Networks
Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency.
Task-oriented Explainable Semantic Communications
Furthermore, based on the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise.
Joint Beamforming and PD Orientation Design for Mobile Visible Light Communications
To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation.
Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver.
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them.
Wyner-Ziv Gradient Compression for Federated Learning
Due to limited communication resources at the client and a massive number of model parameters, large-scale distributed learning tasks suffer from communication bottleneck.
Improved Communication Efficiency for Distributed Mean Estimation with Side Information
We propose a practical and efficient estimator based on an r-bit Wynzer-Ziv estimator proposed by Mayekar et al., which requires no probabilistic assumption on the data.
Information Theory Information Theory
Optimal Coding Scheme and Resource Allocation for Distributed Computation with Limited Resources
In this paper, we study the resource allocation and coding scheme for the MapReduce-type framework with limited resources.
Distributed Computing
Information Theory
Distributed, Parallel, and Cluster Computing
Information Theory
