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 …

The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

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 …

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients
corrupt the global model via sending manipulated model updates to the server. Existing …

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated Learning (FL) as a distributed learning paradigm that aggregates information
from diverse clients to train a shared global model, has demonstrated great success …

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 …