Recently, the ever-growing demand for privacy-oriented machine learning has motivated
researchers to develop federated and decentralized learning techniques, allowing individual …

IoT devices produce a wealth of data desired for learning models to empower more
intelligent applications. However, such data is often privacy sensitive making data owners …

Federated learning (FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

We present a class of methods for robust, personalized federated learning, called Fed+, that
unifies many federated learning algorithms. The principal advantage of this class of methods …

Though successful, federated learning presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …

Despite extensive research into data heterogeneity in federated learning (FL), system
heterogeneity remains a significant yet often overlooked challenge. Traditional FL …

Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order
to protect the privacy of clients. This is typically done using local SGD, which helps to …

Federated learning (FL) is an emerging paradigm in machine learning, where a shared
model is collaboratively learned using data from multiple devices to mitigate the risk of data …

Though successful, federated learning (FL) presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration
stemming from data heterogeneity in federated learning (FL) by grouping similar clients for …