Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Model optimization techniques in personalized federated learning: A survey
Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
The internet of federated things (IoFT)
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …
Improving fairness via federated learning
Recently, lots of algorithms have been proposed for learning a fair classifier from
decentralized data. However, many theoretical and algorithmic questions remain open. First …
decentralized data. However, many theoretical and algorithmic questions remain open. First …
Minimax demographic group fairness in federated learning
Federated learning is an increasingly popular paradigm that enables a large number of
entities to collaboratively learn better models. In this work, we study minimax group fairness …
entities to collaboratively learn better models. In this work, we study minimax group fairness …
Federated learning for green shipping optimization and management
Many shipping companies are unwilling to share their raw data because of data privacy
concerns. However, certain problems in the maritime industry become much more solvable …
concerns. However, certain problems in the maritime industry become much more solvable …
Bias mitigation in federated learning for edge computing
Federated learning (FL) is a distributed machine learning paradigm that enables data
owners to collaborate on training models while preserving data privacy. As FL effectively …
owners to collaborate on training models while preserving data privacy. As FL effectively …
The statistical fairness field guide: perspectives from social and formal sciences
Over the past several years, a multitude of methods to measure the fairness of a machine
learning model have been proposed. However, despite the growing number of publications …
learning model have been proposed. However, despite the growing number of publications …
The current state and challenges of fairness in federated learning
S Vucinich, Q Zhu - IEEE Access, 2023 - ieeexplore.ieee.org
The proliferation of artificial intelligence systems and their reliance on massive datasets
have led to a renewed demand on privacy of data. Both the large data processing need and …
have led to a renewed demand on privacy of data. Both the large data processing need and …