In this paper, we study a new latency optimization problem for blockchain-based federated
learning (BFL) in multi-server edge computing. In this system model, distributed mobile …

Federated learning (FL) has been promoted as a popular technique for training machine
learning (ML) models over edge/fog networks. Traditional implementations of FL have …

The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

Devices located in remote regions often lack coverage from well-developed terrestrial
communication infrastructure. This not only prevents them from experiencing high quality …

While network coverage maps continue to expand, many devices located in remote areas
remain unconnected to terrestrial communication infrastructures, preventing them from …

Federated learning (FL) has emerged as a key technique for distributed machine learning
(ML). Most literature on FL has focused on ML model training for (i) a single task/model, with …

To provide a flexible tradeoff between inference accuracy and resource requirement at
runtime, the slimmable neural network (SNN), a single network executable at different widths …

Federated learning, an emerging machine learning paradigm, enables clients to
collaboratively train a model without exchanging local data. Clients participating in the …

Abstract Resource Scheduling and Distributed learning play a key role in Internet of Things
(IoT) edge computing systems. There has been extensive research in each area, however …

Federated Learning (FL) is an emerging domain in the broader context of artificial
intelligence research. Methodologies pertaining to FL assume distributed model training …