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A survey on federated learning: challenges and applications
Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …
the issue of data silos in building a joint model. Its unique distributed training mode and the …
A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
Towards personalized federated learning
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …
research, there has been growing awareness and concerns of data privacy. Recent …
A survey on security and privacy of federated learning
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
Multi-task federated learning-based system anomaly detection and multi-classification for microservices architecture
J Hao, P Chen, J Chen, X Li - Future Generation Computer Systems, 2024 - Elsevier
The microservices architecture is extensively utilized in cloud-based application
development, characterized by the construction of applications through a series of …
development, characterized by the construction of applications through a series of …
Fedbabu: Towards enhanced representation for federated image classification
Federated learning has evolved to improve a single global model under data heterogeneity
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
An efficient federated distillation learning system for multitask time series classification
This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
Channel attention image steganography with generative adversarial networks
Recently, extensive research has revealed the enormous potential of deep learning in the
application of image steganography. However, some defects still exist in previous studies on …
application of image steganography. However, some defects still exist in previous studies on …
pfl-bench: A comprehensive benchmark for personalized federated learning
Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local
models, has gained increasing attention in recent years due to its success in handling the …
models, has gained increasing attention in recent years due to its success in handling the …