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 …
[HTML][HTML] A survey of security strategies in federated learning: Defending models, data, and privacy
Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …
enabling decentralized model training across multiple devices while preserving data …
A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition
The proliferation of machine learning (ML) applications has given rise to a new and popular
data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining …
data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining …
Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation
Federated Learning (FL) provides a privacy-preserving and decentralized approach to
collaborative machine learning for multiple FL clients. The contribution estimation …
collaborative machine learning for multiple FL clients. The contribution estimation …
Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning
In SplitFed learning (SFL), a global model is split into two segments, where distributed
clients train the first segment in a federated manner and a main server trains the other …
clients train the first segment in a federated manner and a main server trains the other …
Incentivizing Efficient Label Denoising in Federated Learning
Federated learning (FL) is a distributed machine learning scheme that enables clients to
train a shared global model without exchanging local data. In FL, the presence of label noise …
train a shared global model without exchanging local data. In FL, the presence of label noise …
When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation
Federated learning (FL) is decentralized machine learning framework that finds various
applications in health, finance, and the internet of things. This paper studies the under …
applications in health, finance, and the internet of things. This paper studies the under …
Personalized Privacy-Preserving Federated Learning
C Boscher, N Benarba, F Elhattab… - Proceedings of the 25th …, 2024 - dl.acm.org
Federated Learning (FL) enables collaborative model training among several participants
while keeping local data private. However, FL remains vulnerable to privacy membership …
while keeping local data private. However, FL remains vulnerable to privacy membership …
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants
(FL-PTs) to collaborate on training models without sharing private data. Due to data …
(FL-PTs) to collaborate on training models without sharing private data. Due to data …
FedDSL: A Novel Client Selection Method to Handle Statistical Heterogeneity in Cross-Silo Federated Learning Using Flower Framework
V Pais, S Rao, B Muniyal - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning provides a mechanism for different silos to collaborate, and each silo
gets aid without compromising privacy. This simulation study is based on healthcare …
gets aid without compromising privacy. This simulation study is based on healthcare …