Federated learning (FL) is a distributed learning approach, which allows the distributed
computing nodes to collaboratively develop a global model while keeping their data locally …

As an emerging artificial intelligence technology, federated learning plays a significant role
in privacy preservation in machine learning, although its main objective is to prevent peers …

Generally, privacy-preserving distributed deep learning (PPDDL) solutions provided so far
present a trade-off between privacy and efficiency/effectiveness, especially, in terms of high …

Privacy-preserving federated learning (PPFL) enables collaborative model training across
multiple parties while protecting the privacy of sensitive data. However, PPFL is vulnerable …

Federated learning (FL) has recently emerged as an attractive distributed machine learning
paradigm for harnessing the distributed data in edge computing. Its salient feature is that the …

Federated learning (FL) is a promising distributed machine learning scheme where multiple
clients collaborate by sharing a common learning model while maintaining their private data …

The unit of federated learning and differential privacy protection technology can ensure that
the published aggregation has sufficient anonymity. While realizing privacy protection, users' …

In order to solve a series of problems similar to the repetitive construction of resources and
low degree of resource sharing in cruciform teaching, this paper studies the digital disarming …

To mitigate the ever-increasing privacy concerns of model inference, intensive efforts have
been put to develop cryptograph-based private deep learning inference, that preserves the …

Federated learning (FL) allows different participants to collaborate on model training without
transmitting raw data, thereby protecting user data privacy. However, FL faces a series of …