Security and privacy threats to federated learning: Issues, methods, and challenges
Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …
multiple participants to construct a joint model collaboratively without centralizing data. The …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Privacy and robustness in federated learning: Attacks and defenses
As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide
adoption of IoT devices in our daily health management. For IoHT data to be acceptable by …
adoption of IoT devices in our daily health management. For IoHT data to be acceptable by …
When foundation model meets federated learning: Motivations, challenges, and future directions
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
Collaborative fairness in federated learning
In current deep learning paradigms, local training or the Standalone framework tends to
result in overfitting and thus low utility. This problem can be addressed by Distributed or …
result in overfitting and thus low utility. This problem can be addressed by Distributed or …
Towards efficient data free black-box adversarial attack
Classic black-box adversarial attacks can take advantage of transferable adversarial
examples generated by a similar substitute model to successfully fool the target model …
examples generated by a similar substitute model to successfully fool the target model …
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview
The increasing digitization and datification of all aspects of people's daily life, and the
consequent growth in the use of personal data, are increasingly challenging the current …
consequent growth in the use of personal data, are increasingly challenging the current …
Gradient driven rewards to guarantee fairness in collaborative machine learning
In collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self …
together for a common learning task. In realistic CML settings where the agents are self …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …