19 citations · 36 across the 19 of their papers we have counts for
21 papers
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models
Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani +3
Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of v…
Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1
Intent-obfuscation-based jailbreak attacks on multimodal large language models (MLLMs) transform a harmful query into a concealed multimodal input to bypass safety mechanisms. We s…
Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
Yujie Gu, Richeng Jin, Zhaoyang Zhang +1
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In…
Differentially Private and Communication Efficient Large Language Model Split Inference via Stochastic Quantization and Soft Prompt
Yujie Gu, Richeng Jin, Xiaoyu Ji +2
Large Language Models (LLMs) have achieved remarkable performance and received significant research interest. The enormous computational demands, however, hinder the local deployme…
Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
Reza Jahani, Md Farhamdur Reza, Richeng Jin +1
Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central se…
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal…
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
Ferdous Pervej, Richeng Jin, Md Moin Uddin Chowdhury +3
Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors o…
Distribution-Aware Mobility-Assisted Decentralized Federated Learning
Md Farhamdur Reza, Reza Jahani, Richeng Jin +1
Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients ca…
GSBA: - Geometric Score-based Black-box Attack
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1
Existing score-based adversarial attacks mainly focus on crafting -1 adversarial examples against classifiers with single-label classification. Their attack success rate and q…
Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables multiple clients to collaboratively train a global model without sharing their local data. Recent studies have highlighted the vulnerability of FL t…
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Richeng Jin, Yujie Gu, Kai Yue +3
Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Alt…
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
Md Ferdous Pervej, Richeng Jin, Huaiyu Dai
While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery power…
Distributed Learning over Networks with Graph-Attention-Based Personalization
Zhuojun Tian, Zhaoyang Zhang, Zhaohui Yang +2
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribut…
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Richeng Jin, Xiaofan He, Caijun Zhong +3
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods h…
Breaking the Communication-Privacy-Accuracy Tradeoff with -Differential Privacy
Richeng Jin, Zhonggen Su, Caijun Zhong +3
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capab…
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
Md Ferdous Pervej, Richeng Jin, Huaiyu Dai
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (C…
Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands
Md Ferdous Pervej, Richeng Jin, Shih-Chun Lin +1
Modern connected vehicles (CVs) frequently require diverse types of content for mission-critical decision-making and onboard users' entertainment. These contents are required to be…
Communication-Efficient Federated Learning via Predictive Coding
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless m…
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
Richeng Jin, Yufan Huang, Xiaofan He +2
Federated learning (FL) has emerged as a prominent distributed learning paradigm. FL entails some pressing needs for developing novel parameter estimation approaches with theoretic…
Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data
Richeng Jin, Xiaofan He, Huaiyu Dai
The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to…
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
Richeng Jin, Xiaofan He, Huaiyu Dai
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from differe…