Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
Client selection in federated learning: Principles, challenges, and opportunities
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …
learning (FL) has received tremendous attention from both industry and academia. In a …
Federated learning for computationally constrained heterogeneous devices: A survey
With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …
field, offloading training of neural networks (NNs) to a central server becomes more and …
Recent advances on federated learning: A systematic survey
B Liu, N Lv, Y Guo, Y Li - Neurocomputing, 2024 - Elsevier
Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …
collaborative learning among different parties. Compared to traditional centralized learning …
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 …
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 …
A comprehensive survey of federated transfer learning: challenges, methods and applications
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …
participants to collaboratively train a centralized model with privacy preservation by …
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 …
Fwdllm: Efficient federated finetuning of large language models with perturbed inferences
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 …
Timelyfl: Heterogeneity-aware asynchronous federated learning with adaptive partial training
Abstract In cross-device Federated Learning (FL) environments, scaling synchronous FL
methods is challenging as stragglers hinder the training process. Moreover, the availability …
methods is challenging as stragglers hinder the training process. Moreover, the availability …