Federated learning (FL) has become an emerging machine learning technique lately due to
its efficacy in safeguarding the client's confidential information. Nevertheless, despite the …

With the upcoming edge AI, federated learning (FL) is a privacy-preserving framework to
meet the General Data Protection Regulation (GDPR). Unfortunately, FL is vulnerable to an …

Federated learning has attracted attention in recent years due to its native privacy-
preserving features. However, it is still vulnerable to various membership inference attacks …

Recently, federated learning has shown its significant advantages in protecting training data
privacy by maintaining a joint model across multiple clients. However, its model security …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world
scenarios, but this is often overlooked by studies on FL security and privacy. On the one …

Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of
participants to construct a joint ML model without exposing their private training data …

Federated learning (FL) has nourished a promising scheme to solve the data silo, which
enables multiple clients to construct a joint model without centralizing data. The critical …

Adversarial machine learning (AML) has emerged as one of the significant research areas in
machine learning (ML) because models we train lack robustness and trustworthiness …

With the advance in artificial intelligence and high-dimensional data analysis, federated
learning (FL) has emerged to allow distributed data providers to collaboratively learn without …