One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

Federated Learning (FL) under distributed concept drift is a largely unexplored area.
Although concept drift is itself a well-studied phenomenon, it poses particular challenges for …

Federated Learning (FL) is a machine learning paradigm where many clients collaboratively
learn a shared global model with decentralized training data. Personalized FL additionally …

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 …

Data privacy and silos are nontrivial and greatly challenging in many real-world
applications. Federated learning is a decentralized approach to training models across …

Recent advances in deep learning (DL) have shown that data-driven insights can be used in
smart healthcare applications to improve the quality of life for patients. DL needs more data …

Federated learning (FL) is an effective solution to train machine learning models on the
increasing amount of data generated by IoT devices and smartphones while keeping such …

We consider a federated learning (FL) system consisting of multiple clients and a server,
where the clients aim to collaboratively learn a common decision model from their …

Partial client participation has been widely adopted in Federated Learning (FL) to reduce the
communication burden efficiently. However, an inadequate client sampling scheme can lead …

Federated Learning (FL) is an emerging distributed machine learning approach that
preserves client privacy by storing data on edge devices. However, data heterogeneity …