As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

Powerful computer clusters are used nowadays to train complex deep neural networks
(DNN) on large datasets. Distributed training increasingly becomes communication bound …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Abstract Model-agnostic meta-learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure where the outer-loop process learns a …

Gradient compression is a widely-established remedy to tackle the communication
bottleneck in distributed training of large deep neural networks (DNNs). Under the error …

Federated learning (FL) preserves data privacy by exchanging gradients instead of local
training data. However, these private data can still be reconstructed from the exchanged …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …