Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

With the widespread use of distributed machine learning (DML), many IT companies have
established networks dedicated to DML. Different communication architectures of DML have …

As a promising distributed learning paradigm, federated learning (FL) involves training deep
neural network (DNN) models at the network edge while protecting the privacy of the edge …

The exchange of model parameters between peers is critical in peer-to-peer deep learning.
Historically, connections between agents were assigned randomly based on network …

The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation,
establishing a network of devices designed to enrich everyday experiences. Developing …

In recent years, decentralized machine learning has emerged as a significant advancement
in biomedical applications, offering robust solutions for data privacy, security, and …

Abstract Peer-to-peer (P2P) learning is a decentralized approach to organizing the
collaboration between end devices known as agents. Agents contain heterogeneous data …

The field of distributed machine learning (ML) faces increasing demands for scalable and
cost-effective training solutions, particularly in the context of large, complex models …

The advent of serverless computing has ushered in notable advancements in distributed
machine learning, particularly within parameter server-based architectures. Yet, the …

This paper explores the applicability of sequence-to-sequence (Seq2Seq) models based on
LSTM units for Automatic Speech Recognition (ASR) task within peer-to-peer learning …