… which stabilizes the FL algorithm. In addition, we develop an efficient sampling strategy with
the … of the proposed FL algorithm can be guaranteed using a learning rate decay, despite the …

… Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate
in training machine learning (ML) … In this article, we propose a communication-efficient FL …

… dictors is trained via federated learning. This method allows … with on-device data through
federated learning. Motivated by … -efficient ensemble algorithms for federated learning, where …

… To efficiently solve the cost minimization problem, we develop a low-cost sampling-based
algorithm to learn the convergence related unknown parameters. We derive important solution …

… learning frameworks are needed, to enable the wireless devices to collaboratively build a
shared learning … promising distributed learning algorithms is the emerging federated learning (…

… We address the problem of Federated Learning (FL) where users are distributed and partitioned
into clusters. This setup captures settings where different groups of users have their own …

… of the communication efficiency in federated learning. We begin with … federated learning
and the need for communication efficient algorithms and explains that federated learning can …

… in order to perform more efficient federated learning. For this new framework of clustered
federated learning, we propose the Iterative Federated Clustering Algorithm (IFCA), which …

… Abstract Federated learning (FL) ameliorates privacy concerns in settings where a central
server coordinates learning from data distributed across many clients. The clients train locally …

… Federated learning is a powerful distributed learning scheme that allows numerous edge …
first progressive training framework for efficient and effective federated learning. It inherently …