Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Intrusion Detection Systems (IDS) are essential for securing computer networks by
identifying and mitigating potential threats. However, traditional IDS face challenges related …

We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

Data generated at the network edge can be processed locally by leveraging the paradigm of
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …

Transformer-based pre-trained models have revolutionized NLP for superior performance
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …