Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

While recent works have indicated that federated learning (FL) may be vulnerable to
poisoning attacks by compromised clients, their real impact on production FL systems is not …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

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 …

Existing model poisoning attacks to federated learning assume that an attacker has access
to a large fraction of compromised genuine clients. However, such assumption is not realistic …

Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which
malicious clients poison the training process via manipulating their local training data and/or …

Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Recent studies have shown that deep neural net-works (DNNs) are vulnerable to
adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense …

Secure aggregation enables a server to learn the sum of client-held vectors in a privacy-
preserving way, and has been applied to distributed statistical analysis and machine …