5 citations · 18 across the 13 of their papers we have counts for
14 papers
Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach
Muah Kim, Shuangyang Li, Tayyebeh Jahani-Nezhad +2
Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off betw…
SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks
Yue Xia, Tayyebeh Jahani-Nezhad, Mayank Bakshi +1
We consider federated parameter-efficient fine-tuning of foundation models, e.g., LLMs and LVMs, using low-rank adaptation (LoRA; ~Hu et al.\ 2022). Combining LoRA with federated l…
DReS: Dual Reconstruction Smoothing for Functional Regularization
Parsa Moradi, Tayyebeh Jahaninezhad, Hanzaleh Akbarinodehi +1
Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely either on explicit gradient reg…
Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning
Yue Xia, Tayyebeh Jahani-Nezhad, Rawad Bitar
We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our…
Hierarchical Gradient Coding: From Optimal Design to Privacy at Intermediate Nodes
Ali Gholami, Tayyebeh Jahani-Nezhad, Kai Wan +1
Gradient coding is a distributed computing technique for computing gradient vectors over large datasets by outsourcing partial computations to multiple workers, typically connected…
Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace
Tayyebeh Jahani-Nezhad, Parsa Moradi, Mohammad Ali Maddah-Ali +1
Evaluating datasets in data marketplaces, where the buyer aim to purchase valuable data, is a critical challenge. In this paper, we introduce an innovative task-agnostic data valua…
PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
Sizai Hou, Songze Li, Tayyebeh Jahani-Nezhad +1
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typi…
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Giuseppe Caire
In this paper, we propose ByzSecAgg, an efficient secure aggregation scheme for federated learning that is resistant to Byzantine attacks and privacy leakages. Processing individua…
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li +1
We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of distributed users, ea…
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li +1
We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of distributed users, each of size ,…
Optimal Communication-Computation Trade-Off in Heterogeneous Gradient Coding
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali
Gradient coding allows a master node to derive the aggregate of the partial gradients, calculated by some worker nodes over the local data sets, with minimum communication cost, an…
Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali
One of the major challenges in using distributed learning to train complicated models with large data sets is to deal with stragglers effect. As a solution, coded computation has b…
CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali
In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to re…
Performance Analysis of Molecular Spatial Modulation (MSM) in Diffusion based Molecular MIMO Communication Systems
Tayyebeh Jahani-Nezhad, Foroogh S. Tabataba
In diffusion-based molecular communication, information is transferred from a transmitter to a receiver using molecular carriers. The low achievable data rate is the main disadvant…