Abstract The rise of Decentralized Federated Learning (DFL) has enabled the training of
machine learning models across federated participants, fostering decentralized model …

The rapid integration of Federated Learning (FL) into networking encompasses various
aspects such as network management, quality of service, and cybersecurity while preserving …

Federated Learning (FL) offers decentralized machine learning (ML) capabilities while
potentially safeguarding data privacy. However, this architecture introduces unique security …

Federated Learning (FL) has emerged as a powerful paradigm for training Machine
Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while …

Recently emerged federated learning (FL) is an attractive distributed learning framework in
which numerous wireless end-user devices can train a global model with the data remained …

Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data
privacy, security, access rights and access to heterogeneous information issues by training a …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

Advances in distributed machine learning can empower future communications and
networking. The emergence of federated learning (FL) has provided an efficient framework …

Federated learning (FL) enables an effective and private distributed learning process.
However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The …

Federated learning (FL) is a distributed machine learning paradigm that enables training
models on decentralized data. The field of FL security against poisoning attacks is plagued …