A state-of-the-art survey on solving non-iid data in federated learning
Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …
researchers in that it can enable multiple clients to cooperatively train global models without …
Federated analytics: Opportunities and challenges
In this article, we present federated analytics, a new distributed computing paradigm for data
analytics applications with privacy concerns. With the advances of sensing, communication …
analytics applications with privacy concerns. With the advances of sensing, communication …
Data-driven analytics leveraging artificial intelligence in the era of COVID-19: an insightful review of recent developments
A Majeed, SO Hwang - Symmetry, 2021 - mdpi.com
This paper presents the role of artificial intelligence (AI) and other latest technologies that
were employed to fight the recent pandemic (ie, novel coronavirus disease-2019 (COVID …
were employed to fight the recent pandemic (ie, novel coronavirus disease-2019 (COVID …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
FedFPM: A unified federated analytics framework for collaborative frequent pattern mining
Frequent pattern mining is an important class of knowledge discovery problems. It aims at
finding out high-frequency items or structures (eg, itemset, sequence) in a database, and …
finding out high-frequency items or structures (eg, itemset, sequence) in a database, and …
Feddrl: Deep reinforcement learning-based adaptive aggregation for non-iid data in federated learning
The uneven distribution of local data across different edge devices (clients) results in slow
model training and accuracy reduction in federated learning. Naive federated learning (FL) …
model training and accuracy reduction in federated learning. Naive federated learning (FL) …
Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling
Continuous monitoring of patients involves collecting and analyzing sensory data from a
multitude of sources. To overcome communication overhead, ensure data privacy and …
multitude of sources. To overcome communication overhead, ensure data privacy and …
PAGroup: Privacy-aware grouping framework for high-performance federated learning
Federated Learning is designed for multiple mobile devices to collaboratively train an
artificial intelligence model while preserving data privacy. Instead of collecting the raw …
artificial intelligence model while preserving data privacy. Instead of collecting the raw …
Federated analytics informed distributed industrial iot learning with non-iid data
The increasing concerns of communication overheads and data privacy greatly challenge
the gather-and-analyze paradigm of data-driven tasks currently adopted by the industrial IoT …
the gather-and-analyze paradigm of data-driven tasks currently adopted by the industrial IoT …
GraphCS: Graph-based client selection for heterogeneity in federated learning
Federated Learning coordinates many mobile devices to train an artificial intelligence model
while preserving data privacy collaboratively. Mobile devices are usually equipped with …
while preserving data privacy collaboratively. Mobile devices are usually equipped with …