[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
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 …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
[HTML][HTML] Democracy by design: Perspectives for digitally assisted, participatory upgrades of society
The technological revolution, particularly the availability of more data and more powerful
computational tools, has led to the emergence of a new scientific field called “Computational …
computational tools, has led to the emergence of a new scientific field called “Computational …
A survey on decentralized federated learning
In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …
A decentralized federated learning framework via committee mechanism with convergence guarantee
Federated learning allows multiple participants to collaboratively train an efficient model
without exposing data privacy. However, this distributed machine learning training method is …
without exposing data privacy. However, this distributed machine learning training method is …
Survey on federated-learning approaches in distributed environment
Abstract Federated-Learning (FL), a new paradigm in the machine-learning approach,
wherein the clients train the global model collaboratively across various computational …
wherein the clients train the global model collaboratively across various computational …
Federated learning for big data: A survey on opportunities, applications, and future directions
Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …
data generated from newly emerging services and applications and a massive number of …
A comprehensive review of federated learning for COVID‐19 detection
Abstract The coronavirus of 2019 (COVID‐19) was declared a global pandemic by World
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …