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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 …
Mobile edge intelligence for large language models: A contemporary survey
On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …
Accelerating federated learning with model segmentation for edge networks
In the rapidly evolving landscape of distributed learning strategies, Federated Learning (FL)
stands out for its features such as model training on resource-constrained edge devices and …
stands out for its features such as model training on resource-constrained edge devices and …
Federated learning with flexible control
S Wang, J Perazzone, M Ji… - IEEE INFOCOM 2023 …, 2023 - ieeexplore.ieee.org
Federated learning (FL) enables distributed model training from local data collected by
users. In distributed systems with constrained resources and potentially high dynamics, eg …
users. In distributed systems with constrained resources and potentially high dynamics, eg …
FedAPT: Joint adaptive parameter freezing and resource allocation for communication-efficient federated vehicular networks
Telematics technology development offers vehicles a range of intelligent and convenient
functions, including navigation and map** services, intelligent driving assistance, and …
functions, including navigation and map** services, intelligent driving assistance, and …
Fedagl: A communication-efficient federated vehicular network
With the development of the technologies deployed on vehicles, there is a significant
increase in the amount of data, which comes from various applications, such as battery …
increase in the amount of data, which comes from various applications, such as battery …
Perfedmask: Personalized federated learning with optimized masking vectors
M Setayesh, X Li, VWS Wong - The Eleventh International …, 2022 - openreview.net
Recently, various personalized federated learning (FL) algorithms have been proposed to
tackle data heterogeneity. To mitigate device heterogeneity, a common approach is to use …
tackle data heterogeneity. To mitigate device heterogeneity, a common approach is to use …
Spherefed: Hyperspherical federated learning
Federated Learning aims at training a global model from multiple decentralized devices (ie
clients) without exchanging their private local data. A key challenge is the handling of non …
clients) without exchanging their private local data. A key challenge is the handling of non …
Egeria: Efficient dnn training with knowledge-guided layer freezing
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try
to overlap/schedule computation and communication for efficient training, this paper goes …
to overlap/schedule computation and communication for efficient training, this paper goes …
Joint optimization of energy consumption and completion time in federated learning
Federated Learning (FL) is an intriguing distributed machine learning approach due to its
privacy-preserving characteristics. To balance the trade-off between energy and execution …
privacy-preserving characteristics. To balance the trade-off between energy and execution …