Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Communication compression, a technique aiming to reduce the information volume to be
transmitted over the air, has gained great interests in Federated Learning (FL) for the …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Decentralized optimization strategies are helpful for various applications, from networked
estimation to distributed machine learning. This paper studies finite-sum minimization …

Current data compression methods, such as sparsification in Federated Averaging
(FedAvg), effectively enhance the communication efficiency of Federated Learning (FL) …

Federated Learning (FL) offers a promising framework for collaborative and privacy-
preserving machine learning across distributed data sources. However, the substantial …

In practical federated learning (FL) systems, the presence of malicious Byzantine attacks
and data heterogeneity often introduces biases into the learning process. However, existing …

Distributed learning is an effective approach to accelerate model training using multiple
workers. However, substantial communication delays emerge between workers and a …

In a real federated learning (FL) system, communication overhead for passing model
parameters between the clients and the parameter server (PS) is often a bottleneck …

This paper introduces a novel framework designed to achieve a high compression ratio in
Split Learning (SL) scenarios where resource-constrained devices are involved in large …