Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

In this article, we propose and analyze SParsified Action Regulated Quantized–Stochastic
Gradient Descent (SPARQ-SGD), a communication-efficient algorithm for decentralized …

Privacy protection and nonconvexity are two challenging problems in decentralized
optimization and learning involving sensitive data. Despite some recent advances …

This article is concerned with the distributed convex constrained optimization over a time-
varying multiagent network in the non-Euclidean sense, where the bandwidth limitation of …

This article proposes an event-triggered adaptive gradient descent method for distributed
online optimization with a constraint set. In the proposed method, a group of agents …

This article considers a game-theoretic framework for distributed empirical risk minimization
(ERM) problems over networks where the information acquisition at a node is modeled as a …

This paper is concerned with an online distributed convex-constrained optimization problem
over a multi-agent network, where the limited network bandwidth and potential feedback …

In this article, we study a decentralized online constrained optimization problem with a
common constraint set, in which the main purpose is to optimize the problem over a certain …

Federated learning (FL) is a collaborative machine learning (ML) paradigm based on
persistent communication between a central server and multiple edge devices. However …