Decision trees in federated learning: Current state and future opportunities
SR Heiyanthuduwage, I Altas, M Bewong… - IEEE …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning technique that enables multiple
decentralized clients to develop a model collaboratively without exchanging their local data …
decentralized clients to develop a model collaboratively without exchanging their local data …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
[HTML][HTML] Enabling federated learning of explainable AI models within beyond-5G/6G networks
The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in
the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will …
the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will …
Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson's Disease
Artificial intelligence (AI) systems are increasingly used in healthcare applications, although
some challenges have not been completely overcome to make them fully trustworthy and …
some challenges have not been completely overcome to make them fully trustworthy and …
[HTML][HTML] Increasing trust in AI through privacy preservation and model explainability: Federated Learning of Fuzzy Regression Trees
Federated Learning (FL) lets multiple data owners collaborate in training a global model
without any violation of data privacy, which is a crucial requirement for enhancing users' trust …
without any violation of data privacy, which is a crucial requirement for enhancing users' trust …
Experimenting with normalization layers in federated learning on non-iid scenarios
B Casella, R Esposito, A Sciarappa, C Cavazzoni… - IEEE …, 2024 - ieeexplore.ieee.org
Training Deep Learning (DL) models require large, high-quality datasets, often assembled
with data from different institutions. Federated Learning (FL) has been emerging as a …
with data from different institutions. Federated Learning (FL) has been emerging as a …
Benchmarking fedavg and fedcurv for image classification tasks
B Casella, R Esposito, C Cavazzoni… - arXiv preprint arXiv …, 2023 - arxiv.org
Classic Machine Learning techniques require training on data available in a single data
lake. However, aggregating data from different owners is not always convenient for different …
lake. However, aggregating data from different owners is not always convenient for different …
Model-agnostic federated learning
Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of
Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but …
Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but …
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 …
[HTML][HTML] OpenFL-XAI: Federated learning of explainable artificial intelligence models in Python
Artificial Intelligence (AI) systems play a significant role in manifold decision-making
processes in our daily lives, making trustworthiness of AI more and more crucial for its …
processes in our daily lives, making trustworthiness of AI more and more crucial for its …