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 …
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 …
Generalized federated learning via sharpness aware minimization
Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …
distributed learning with a set of clients. However, the data distribution among clients often …
Fairfed: Enabling group fairness in federated learning
Training ML models which are fair across different demographic groups is of critical
importance due to the increased integration of ML in crucial decision-making scenarios such …
importance due to the increased integration of ML in crucial decision-making scenarios such …
Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems
Federated learning is an emerging privacy-preserving AI technique where clients (ie,
organizations or devices) train models locally and formulate a global model based on the …
organizations or devices) train models locally and formulate a global model based on the …
Fair federated medical image segmentation via client contribution estimation
How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …
investigated how to reward clients based on their contribution (collaboration fairness), and …
Addressing algorithmic disparity and performance inconsistency in federated learning
Federated learning (FL) has gain growing interests for its capability of learning from
distributed data sources collectively without the need of accessing the raw data samples …
distributed data sources collectively without the need of accessing the raw data samples …
Towards fairness-aware federated learning
Recent advances in federated learning (FL) have brought large-scale collaborative machine
learning opportunities for massively distributed clients with performance and data privacy …
learning opportunities for massively distributed clients with performance and data privacy …
Online client selection for asynchronous federated learning with fairness consideration
Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …
to collaboratively train a global model. Many existing FL algorithms over wireless networks …
Preserving the fairness guarantees of classifiers in changing environments: a survey
The impact of automated decision-making systems on human lives is growing, emphasizing
the need for these systems to be not only accurate but also fair. The field of algorithmic …
the need for these systems to be not only accurate but also fair. The field of algorithmic …