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 …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
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 …
Refl: Resource-efficient federated learning
AM Abdelmoniem, AN Sahu, M Canini… - Proceedings of the …, 2023 - dl.acm.org
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 …
Fedgroup: Efficient federated learning via decomposed similarity-based clustering
Federated Learning (FL) enables the multiple participating devices to collaboratively
contribute to a global neural network model while keeping the training data locally. Unlike …
contribute to a global neural network model while keeping the training data locally. Unlike …
Federated learning with workload-aware client scheduling in heterogeneous systems
Federated Learning (FL) is a novel distributed machine learning, which allows thousands of
edge devices to train models locally without uploading data to the central server. Since …
edge devices to train models locally without uploading data to the central server. Since …
Fedcare: Federated learning for resource-constrained healthcare devices in iomt system
In social IoMT systems, resource-constrained devices face the challenges of limited
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …
A review of client selection methods in federated learning
S Mayhoub, T M. Shami - Archives of Computational Methods in …, 2024 - Springer
Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …
models to be trained locally on edge devices while preserving the privacy of the devices' …
Heterogeneous Defect Prediction Based on Federated Prototype Learning
A Wang, L Yang, H Wu, Y Iwahori - IEEE Access, 2023 - ieeexplore.ieee.org
Software defect prediction is used to identify modules in software projects that may have
defects. Heterogeneous Defect Prediction (HDP) establishes a cross project defect …
defects. Heterogeneous Defect Prediction (HDP) establishes a cross project defect …
Heterogeneous Training Intensity for federated learning: A Deep reinforcement learning Approach
Federated learning (FL) has recently received considerable attention in Internet of Things,
due to its capability of letting multiple clients collaboratively train machine learning models …
due to its capability of letting multiple clients collaboratively train machine learning models …