Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
[HTML][HTML] FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments
Massive data collection in modern systems has paved the way for data-driven machine
learning, a promising technique for creating reliable and robust statistical models. By …
learning, a promising technique for creating reliable and robust statistical models. By …
Review on security of federated learning and its application in healthcare
Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …
progress has been made on many complex medical problems. However, there is a lack of …
Make landscape flatter in differentially private federated learning
To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …
Improving the model consistency of decentralized federated learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …
decentralized FL (DFL) discards the central server and each client only communicates with …
Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective
Accurate remaining useful life (RUL) prediction has gained increasing attention in modern
maintenance management. Considering the data privacy requirements of distributed multi …
maintenance management. Considering the data privacy requirements of distributed multi …
Federated learning over wireless device-to-device networks: Algorithms and convergence analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …
siloed data centers is motivating renewed interest in the collaborative training of a shared …
BRIDGE: Byzantine-resilient decentralized gradient descent
Machine learning has begun to play a central role in many applications. A multitude of these
applications typically also involve datasets that are distributed across multiple computing …
applications typically also involve datasets that are distributed across multiple computing …
Stability-based generalization analysis of the asynchronous decentralized SGD
The generalization ability often determines the success of machine learning algorithms in
practice. Therefore, it is of great theoretical and practical importance to understand and …
practice. Therefore, it is of great theoretical and practical importance to understand and …
A collective AI via lifelong learning and sharing at the edge
A Soltoggio, E Ben-Iwhiwhu, V Braverman… - Nature Machine …, 2024 - nature.com
One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …
independently over a lifetime and share their knowledge with each other. The synergy …