Search Results for author:
Found 16 papers, 5 papers with code
Federated Computing -- Survey on Building Blocks, Extensions and Systems
FC comprises of Federated Learning (FL) and Federated Analytics (FA).
Federated Learning Priorities Under the European Union Artificial Intelligence Act
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way.
Choosing a Classical Planner with Graph Neural Networks
Thus, the ability to predict their performance on a given problem is of great importance.
A Survey on Efficient Federated Learning Methods for Foundation Model Training
We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster.
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
In this paper, we study the effectiveness of graph partitioning for distributed GNN training.
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Federated Machine Learning (FL) has received considerable attention in recent years.
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers?
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
Graph Neural Networks (GNNs) are an emerging research field.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.
The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data
The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry.
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
As a consequence of this development, data preprocessing and provisioning are becoming a severe bottleneck in end-to-end deep learning pipelines.
Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools
One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available.
A Comprehensive Survey on Parallelization and Elasticity in Stream Processing
Parallelization and elasticity enables SP systems to process these streams with continuously high quality of service.
Distributed, Parallel, and Cluster Computing
HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion
In such a model, vertices represent entities-such as users or data records-whereas hyperedges model a group membership of the vertices-such as the authorship in a specific topic or the membership of a data record in a specific replicated shard.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Social and Information Networks
