Search Results for author:
Found 31 papers, 21 papers with code
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs).
Exploiting Label Skews in Federated Learning with Model Concatenation
However, non-IID data has been a key challenge in FL, which could significantly degrade the accuracy of the final model.
Efficient Heterogeneous Graph Learning via Random Projection
To achieve low information loss, we introduce a Relation-wise Neighbor Collection component with an Even-odd Propagation Scheme, which aims to collect information from neighbors in a finer-grained way.
Ranked #3 on
Node Property Prediction
on ogbn-mag
Effective and Efficient Federated Tree Learning on Hybrid Data
To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data.
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data.
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs.
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Thus, a natural question is how those open environment challenges look like and how existing incremental learning algorithms perform on real-world relational data streams.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users.
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions.
BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection
As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized.
HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks
However, due to the gigantic search space and lack of intelligent search guidance, current auto-schedulers require hours to days of tuning time to find the best-performing tensor program for the entire neural network.
Practical Vertical Federated Learning with Unsupervised Representation Learning
As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications.
Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors.
A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources.
Adversarial Collaborative Learning on Non-IID Features
Federated learning has been a popular approach to enable collaborative learning on multiple parties without exchanging raw data.
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
However, most existing studies in VFL disregard the "record linkage" process.
Model-Contrastive Federated Learning
A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties.
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload
The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study.
Federated Learning on Non-IID Data Silos: An Experimental Study
We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases.
Practical One-Shot Federated Learning for Cross-Silo Setting
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data.
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer
In this paper, we propose a novel federated learning algorithm FedKT that needs only a single communication round (i. e., round-optimal).
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems.
Privacy-Preserving Gradient Boosting Decision Trees
Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds.
Practical Federated Gradient Boosting Decision Trees
There have been several recent studies on how to train GBDTs in the federated learning setting.
Adaptive Kernel Value Caching for SVM Training
Our experimental results show that EFU often has 20\% higher hit ratio than LRU in the training with the Gaussian kernel.
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
Accelerating Generative Neural Networks on Unmodified Deep Learning Processors -- A Software Approach
Generative neural network is a new category of neural networks and it has been widely utilized in applications such as content generation, unsupervised learning, segmentation and pose estimation.
A Survey on Graph Processing Accelerators: Challenges and Opportunities
Graph is a well known data structure to represent the associated relationships in a variety of applications, e. g., data science and machine learning.
Distributed, Parallel, and Cluster Computing
Efficient Memory Management for GPU-based Deep Learning Systems
They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables.
