This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Federated learning is a multiple device collaboration setup designed to solve machine
learning problems under framework for aggregation and knowledge transfer in distributed …

Concerned with user data privacy, this paper presents a new federated learning (FL) method
that trains machine learning models on edge devices without accessing sensitive data …

Recently, local peer topology has been shown to influence the overall convergence of
decentralized learning (DL) graphs in the presence of data heterogeneity. In this paper, we …

Federated learning (FL) is a machine learning paradigm where multiple clients collaborate
to optimize a single global model using their private data. The global model is maintained by …

Data heterogeneity impacts the performance of Federated Learning (FL) by introducing
training noise. Although representative client sampling can help mitigate the issue, it …

Advancements in reinforcement learning (RL) via deep neural networks have enabled their
application to a variety of real-world problems. However, these applications often suffer from …

Federated Learning (FL) is a machine learning approach that addresses privacy and data
transfer costs by computing data at the source. It's particularly popular for Edge and IoT …

Massive amounts of data are produced continuously by billions of Internet of Things (IoT)
devices and analyzed via Machine Learning (ML) models to serve a wide variety of needs …

Recently, local peer topology has been shown to influence the overall convergence of
decentralized learning (DL) graphs in the presence of data heterogeneity. In this paper, we …