Search Results for author: Yu-Chih Huang

Found 7 papers, 3 papers with code

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

no code implementations10 Aug 2023 Aamer Mohamed Huroon, Yu-Chih Huang, Li-Chun Wang

A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders.

Committed Private Information Retrieval

1 code implementation3 Feb 2023 Quang Cao, Hong Yen Tran, Son Hoang Dau, Xun Yi, Emanuele Viterbo, Chen Feng, Yu-Chih Huang, Jingge Zhu, Stanislav Kruglik, Han Mao Kiah

A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data.

Information Retrieval Retrieval

How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct

no code implementations18 Apr 2022 Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini

Namely: (i) gradient quantization through floating-point conversion, (ii) lossless compression of the quantized gradient, and (iii) quantization error correction.

Quantization

Convert, compress, correct: Three steps toward communication-efficient DNN training

1 code implementation17 Mar 2022 Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini

In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training.

Quantization

DNN gradient lossless compression: Can GenNorm be the answer?

1 code implementation15 Nov 2021 Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini

In this paper we argue that, for some networks of practical interest, the gradient entries can be well modelled as having a generalized normal (GenNorm) distribution.

Federated Learning

Wireless Federated Learning with Limited Communication and Differential Privacy

no code implementations1 Jun 2021 Amir Sonee, Stefano Rini, Yu-Chih Huang

This paper investigates the role of dimensionality reduction in efficient communication and differential privacy (DP) of the local datasets at the remote users for over-the-air computation (AirComp)-based federated learning (FL) model.

Dimensionality Reduction Federated Learning

Iterative Collision Resolution for Slotted ALOHA with NOMA for Heterogeneous Devices

no code implementations9 Dec 2020 Yu-Chih Huang, Shin-Lin Shieh, Yu-Pin Hsu, Hao-Ping Cheng

In this paper, the problem of using uncoordinated multiple access (UMA) to serve a massive amount of heterogeneous users is investigated.

Information Theory Information Theory

Cannot find the paper you are looking for? You can Submit a new open access paper.