activity
20202026
most citedFedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

37 citations · 69 across the 20 of their papers we have counts for

collaborators

20 papers

cs.IT2026

The Capacity Region of the Multiple Access Channel with Non-Signaling Assistance

Yuhang Yao, Syed A. Jafar

The capacity region of the -sender discrete memoryless multiple access channel (MAC) is fully characterized when non-signaling (NS) assistance is available to all transmitte…

cs.IT2026

The Capacity Region of the Broadcast Channel with Non-Signaling Assistance

Yuhang Yao, Syed A. Jafar

The capacity region of the -user discrete memoryless broadcast channel is fully characterized when non-signaling (NS) assistance is available to the transmitter and all rece…

cs.IT2026

Unlocking Exponential and Unbounded Robust Gains in Shannon Capacity of Classical Multiple Access Channels with Causal CSIT via Quantum Entanglement Assistance

Yuhang Yao, Syed A. Jafar

Quantum entanglement assistance is known to improve the Shannon capacity of classical communication networks but the largest gains noted thus far are rather modest (less than 6%),…

quant-ph2026

Quantum Entanglement Assistance Improves the Capacity and Activates the Zero-Error Capacity of Classical Channels with Causal CSIT

Yuhang Yao, Syed A. Jafar

For classical point-to-point channels, it has been shown by Bennett et al. that quantum entanglement assistance cannot improve their capacity, and by Cubitt et al. that entanglemen…

quant-ph2026

Space-sharing and Singleton Bounds for Entanglement-assisted Classical Coding

Yuhang Yao, Tushita Prasad, Markus Grassl +2

Recent work has noted that a space-sharing argument proves the tightness of the entropic quantum Singleton bounds, which was left open in the literature for various settings involv…

cs.IT2026

Non-signaling Assisted Capacity of a Classical Channel with Causal CSIT

Yuhang Yao, Syed A. Jafar

The non-signaling (NS) assisted capacity of a classical discrete memoryless channel with causal channel state information at the transmitter (CSIT) is shown to be $C^{NS,ca}=\max_{…

cs.IT2025

Virtual Signaling of CSIT via Non-Signaling Assistance

Yuhang Yao, Syed A. Jafar

Non-signaling correlations, which (strictly) include quantum correlations, provide a tractable path to explore the potential impact of quantum nonlocality on the capacity of classi…

quant-ph2025

On the Utility of Quantum Entanglement for Joint Communication and Instantaneous Detection

Yuhang Yao, Syed A. Jafar

Entanglement is known to significantly improve the performance (separately) of communication and detection schemes that utilize quantum resources. This work explores the simultaneo…

cs.IT2025

Can Non-Signaling Assistance Increase the Degrees of Freedom of a Wireless Network?

Yuhang Yao, Syed A. Jafar

An open question posed by Fawzi and Ferme [Transactions on Information Theory 2024], asks whether non-signaling (NS) assistance can increase the capacity of a broadcast channel (BC…

cs.IT2024★ 2 cited

On the Capacity of Vector Linear Computation over a Noiseless Quantum Multiple Access Channel with Entangled Transmitters

Yuhang Yao, Syed A. Jafar

Network function computation is an active topic in network coding, with much recent progress for linear (over a finite field) computations over broadcast (LCBC) and multiple access…

cs.IT2023

Capacity of Summation over a Symmetric Quantum Erasure MAC with Partially Replicated Inputs

Yuhang Yao, Syed A. Jafar

The optimal quantum communication cost of computing a classical sum of distributed sources is studied over a quantum erasure multiple access channel (QEMAC). K classical messages c…

cs.IT2023★ 7 cited

On the Capacity of Secure -user Product Computation over a Quantum MAC

Yuxiang Lu, Yuhang Yao, Syed A. Jafar

Inspired by recent work by Christensen and Popovski on secure -user product computation for finite-fields of prime-order over a quantum multiple access channel, the generalizati…

cs.IT2023★ 10 cited

The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Distributed Inputs and Entanglements

Yuhang Yao, Syed A. Jafar

The -QMAC problem is introduced, involving servers, classical () data streams, and independent quantum systems. Data stream is rep…

cs.IT2023

-Sum Box: An Abstraction for Linear Computation over Many-to-one Quantum Networks

Matteo Allaix, Yuxiang Lu, Yuhang Yao +3

Linear computations over quantum many-to-one communication networks offer opportunities for communication cost improvements through schemes that exploit quantum entanglement among…

cs.LG2023★ 37 cited

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

Weizhao Jin, Yuhang Yao, Shanshan Han +5

Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated…

cs.LG2022

FedRule: Federated Rule Recommendation System with Graph Neural Networks

Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng +3

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera…

cs.IT2022★ 6 cited

On the Generic Capacity of -User Symmetric Linear Computation Broadcast

Yuhang Yao, Syed A. Jafar

Linear computation broadcast (LCBC) refers to a setting with dimensional data stored at a central server, where users, each with some prior linear side-information, wish to…

cs.IT2022★ 6 cited

The Capacity of 3 User Linear Computation Broadcast

Yuhang Yao, Syed A. Jafar

The User Linear Computation Broadcast (LCBC) problem is comprised of dimensional data (from ), that is fully available to a central server, and users, who…

cs.LG2022

Faithful Explanations for Deep Graph Models

Zifan Wang, Yuhang Yao, Chaoran Zhang +5

This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations f…

cs.LG2020★ 1 cited

Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

Yuhang Yao, Carlee Joe-Wong

We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time, e.g., due to community migrati…