Recently, decentralized learning has been proposed for model training among mobile
devices without center nodes. However, large resource overhead for model aggregation and …

Federated learning (FL) is envisioned to be a key enabler of network functionalities based
on artificial intelligence. Multiple access mechanisms supporting the learning task must then …

With the development of information technology, network data security issues have received
widespread attention. Under traditional wired communication networks, it not only has high …

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era,
where high data rates and low latency are critical, D2D communication is a pivotal …

In this paper we propose the federated private local training algorithm (Fed-PLT) for
federated learning, to overcome the challenges of (i) expensive communications and (ii) …

Decentralized optimization has become a standard paradigm for solving large-scale
decision-making problems and training large machine learning models without centralizing …

In the context of distributed deep learning, the issue of stale weights or gradients could result
in poor algorithmic performance. This issue is usually tackled by delay tolerant algorithms …

In this paper we consider online distributed learning problems. Online distributed learning
refers to the process of training learning models on distributed data sources. In our setting a …

Developing, implementing, and maintaining a multi-intelligent body collaboration system
necessitates significant investments in finances, time, and expertise. While multi-intelligent …

The demand for machine learning (ML) algorithms in wireless communication have
increased over the last decade. Anyhow to enhance the prediction quality in complex …