4 citations · 8 across the 10 of their papers we have counts for
13 papers
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 large neural networks with low-rank adaptation (LoRA,~Hu et al.\ 2022). Combining LoRA with federated PEFT introduces chall…
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…