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Topology-aware federated learning in edge computing: A comprehensive survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …
distributed machine learning systems to be deployed at the edge. With its simple yet …
Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data
Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
Communication-efficient large-scale distributed deep learning: A comprehensive survey
F Liang, Z Zhang, H Lu, V Leung, Y Guo… - ar** data distribution at edge
Y Deng, F Lyu, T **a, Y Zhou, Y Zhang… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
Federated learning (FL) enables collaborative model training over distributed computing
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …
Coopfl: Accelerating federated learning with dnn partitioning and offloading in heterogeneous edge computing
Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …
Efficient client selection based on contextual combinatorial multi-arm bandits
To overcome the challenge of limited bandwidth, client selection has been considered an
effective method for optimizing Federated Learning (FL). However, since the volatility of the …
effective method for optimizing Federated Learning (FL). However, since the volatility of the …
Agglomerative federated learning: Empowering larger model training via end-edge-cloud collaboration
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …
without compromising their privacy. As computing tasks are increasingly performed by a …
FedUC: A unified clustering approach for hierarchical federated learning
Federated learning (FL) is an effective approach to train models collaboratively among
distributed edge nodes (ie, workers) while facing three crucial challenges, edge …
distributed edge nodes (ie, workers) while facing three crucial challenges, edge …
Federated Edge Learning for 6G: Foundations, Methodologies, and Applications
Artificial intelligence (AI) is envisioned to be natively integrated into the sixth-generation (6G)
mobile networks to support a diverse range of intelligent applications. Federated edge …
mobile networks to support a diverse range of intelligent applications. Federated edge …
Federated fusion learning with attention mechanism for multi-client medical image analysis
Federated Learning (FL) has gained significant attention because of its potential for privacy-
preserving distributed learning. However, statistical heterogeneity and label scarcity remain …
preserving distributed learning. However, statistical heterogeneity and label scarcity remain …