In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Tokenized Intelligence (TI) aims to enhance network performance by combining machine
learning and blockchain-based tokenomics in software-defined networks (5G/6G). This …

Blockchain promises to enhance distributed machine learning (ML) approaches such as
federated learning (FL) by providing further decentralization, security, immutability, and trust …

This paper presents a fully coupled blockchain-assisted federated learning architecture that
effectively eliminates single points of failure by decentralizing both the training and …

This book chapter explores the intersection of Blockchain Technology and Federated
Learning, presenting a comprehensive overview of their synergistic potential in creating …

Decentralized federated learning across edge networks can leverage blockchain with
consensus mechanisms for training information exchange among participants over costly …