Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …

Edge intelligence is an emerging technology that enables artificial intelligence on connected
systems and devices in close proximity to the data sources. Decentralized Collaborative …

Decentralized learning (DL) has gained prominence for its potential benefits in terms of
scalability, privacy, and fault tolerance. It consists of many nodes that coordinate without a …

Decentralized learning (DL) systems have been gaining popularity because they avoid raw
data sharing by communicating only model parameters, hence preserving data …

Forking attacks against TEEs like Intel SGX can be carried out either by rolling back the
application to a previous state, or by cloning the application and by partitioning its inputs …

Personalized learning is a proposed approach to address the problem of data heterogeneity
in collaborative machine learning. In a decentralized setting, the two main challenges of …

Decentralized learning (DL) enables collaborative learning without a server and without
training data leaving the users' devices. However, the models shared in DL can still be used …

Federated learning (FL) is a promising paradigm for training a global model over data
distributed across multiple data owners without centralizing clients' raw data. However …

Federated Learning (FL) is a machine learning approach where nodes collaboratively train
a global model. As more nodes participate in a round of FL, the effectiveness of individual …

SkipTrain is a novel Decentralized Learning (DL) algorithm, which minimizes energy
consumption in decentralized learning by strategically skipping some training rounds and …