Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
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 …
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 …
Communication-efficient distributed deep learning: A comprehensive survey
Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …
A survey on decentralized federated learning
E Gabrielli, G Pica, G Tolomei - arXiv preprint arXiv:2308.04604, 2023 - arxiv.org
In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …
Fusionai: Decentralized training and deploying llms with massive consumer-level gpus
The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …
Emerging trends in federated learning: From model fusion to federated x learning
Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …
training via multi-party computation and model aggregation. As a flexible learning setting …
Fedcs: Efficient communication scheduling in decentralized federated learning
R Zong, Y Qin, F Wu, Z Tang, K Li - Information Fusion, 2024 - Elsevier
Decentralized federated learning is a training approach that prioritizes user data privacy
protection, while also offering improved scalability and robustness. However, as the number …
protection, while also offering improved scalability and robustness. However, as the number …
Energy-aware, device-to-device assisted federated learning in edge computing
The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent
Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time …
Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time …
VARF: An incentive mechanism of cross-silo federated learning in MEC
Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning
where organizations acting as clients cooperatively train a global model without uploading …
where organizations acting as clients cooperatively train a global model without uploading …