Search Results for author:
Found 22 papers, 7 papers with code
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos
Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information.
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving
Given the wide adoption of multimodal sensors (e. g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative.
PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data
Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose a Privacy-preserving Diversified Service Recommendation (PDSR) method.
Federating to Grow Transformers with Constrained Resources without Model Sharing
To the best of our knowledge, most of the previous model-scaling works are centralized, and our work is the first one that cooperatively grows a transformer from multiple pre-trained heterogeneous models with the user privacy protected in terms of local data and models.
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee
The safety of decentralized reinforcement learning (RL) is a challenging problem since malicious agents can share their poisoned policies with benign agents.
A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics
Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with \emph{noisy} and \emph{incomplete} count data.
Communication Efficient and Provable Federated Unlearning
We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data.
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
Federated learning is a powerful technique that enables collaborative learning among different clients.
Byzantine-Resilient Federated Learning at Edge
To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously.
A Self-Correcting Sequential Recommender
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
Modeling Sequential Recommendation as Missing Information Imputation
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
Federated Learning Hyper-Parameter Tuning from a System Perspective
In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.
Collaborative Learning in General Graphs with Limited Memorization: Complexity, Learnability, and Reliability
The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost.
Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models.
A Systematic Survey of Blockchained Federated Learning
Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL.
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition
On the one hand, multiple views across tasks possibly relate to each other under practical situations.
DCAP: Deep Cross Attentional Product Network for User Response Prediction
Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly.
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture.
Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm
However, the separation between the data usufruct and ownership in Blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning.
Cryptography and Security
Meta Matrix Factorization for Federated Rating Predictions
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
