Trustworthy federated learning: a comprehensive review, architecture, key challenges, and future research prospects
Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …
Intelligence (AI), enabling collaborative model training across distributed devices while …
Advances and open challenges in federated learning with foundation models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
Toxicity, morality, and speech act guided stance detection
In this work, we focus on the task of determining the public attitude toward various social
issues discussed on social media platforms. Platforms such as Twitter, however, are often …
issues discussed on social media platforms. Platforms such as Twitter, however, are often …
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
The joint implementation of Federated learning (FL) and Explainable artificial intelligence
(XAI) will allow training models from distributed data and explaining their inner workings …
(XAI) will allow training models from distributed data and explaining their inner workings …
ST-Tree with interpretability for multivariate time series classification
Multivariate time series classification is of great importance in practical applications and is a
challenging task. However, deep neural network models such as Transformers exhibit high …
challenging task. However, deep neural network models such as Transformers exhibit high …
Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training
Federated learning (FL) is undergoing significant traction due to its ability to perform privacy-
preserving training on decentralized data. In this work, we focus on sensitive time series …
preserving training on decentralized data. In this work, we focus on sensitive time series …
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
Sourced from various sensors and organized chronologically, Multivariate Time-Series
(MTS) data involves crucial spatial-temporal dependencies, eg, correlations among sensors …
(MTS) data involves crucial spatial-temporal dependencies, eg, correlations among sensors …
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
Granger causality, commonly used for inferring causal structures from time series data, has
been adopted in widespread applications across various fields due to its intuitive …
been adopted in widespread applications across various fields due to its intuitive …
FedST: secure federated shapelet transformation for time series classification
Z Liang, H Wang - The VLDB Journal, 2024 - Springer
This paper explores how to build a shapelet-based time series classification (TSC) model in
the federated learning (FL) scenario, that is, using more data from multiple owners without …
the federated learning (FL) scenario, that is, using more data from multiple owners without …
Privacy-Preserving Federated Interpretability
AA Fahliani, A Aminifar… - … Conference on Big Data …, 2024 - portal.research.lu.se
Interpretability has become a crucial component in the Machine Learning (ML) domain. This
is particularly important in the context of medical and health applications, where the …
is particularly important in the context of medical and health applications, where the …