Federated learning in edge computing: a systematic survey
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 …
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …
Survey on federated learning for intrusion detection system: Concept, architectures, aggregation strategies, challenges, and future directions
Intrusion Detection Systems (IDS) are essential for securing computer networks by
identifying and mitigating potential threats. However, traditional IDS face challenges related …
identifying and mitigating potential threats. However, traditional IDS face challenges related …
Fedscale: Benchmarking model and system performance of federated learning at scale
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …
A comprehensive empirical study of heterogeneity in federated learning
Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …
distributed, private data sets owned by nontrusting entities. FL has seen successful …
Adaptive control of local updating and model compression for efficient federated learning
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 …
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …
Refl: Resource-efficient federated learning
Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …
enhancing privacy and reducing communication. However, it presents numerous challenges …
Context-aware online client selection for hierarchical federated learning
Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …
Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients
Federated Learning (FL) trains a machine learning model on distributed clients without
exposing individual data. Unlike centralized training that is usually based on carefully …
exposing individual data. Unlike centralized training that is usually based on carefully …
Accelerating federated learning with data and model parallelism in edge computing
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 …
edge. Federated Learning, as an emerging technique for edge AI, has been widely …
Federated fine-tuning of billion-sized language models across mobile devices
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 …
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …