Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning
J Liu, C Chen, Y Li, L Sun, Y Song, J Zhou… - … and Information Systems, 2024 - Springer
While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …
approaches like blockchain offer a compelling solution by implementing a consensus …
Aedfl: efficient asynchronous decentralized federated learning with heterogeneous devices
Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …
collaborative model training on distributed data over edge devices. Iterative gradient or …
Efficient asynchronous federated learning with sparsification and quantization
While data is distributed in multiple edge devices, federated learning (FL) is attracting more
and more attention to collaboratively train a machine learning model without transferring raw …
and more attention to collaboratively train a machine learning model without transferring raw …
Federated learning in heterogeneous wireless networks with adaptive mixing aggregation and computation reduction
Despite the recent advancements achieved by federated learning (FL), its real-world
deployment is significantly impeded by the heterogeneous learning environment …
deployment is significantly impeded by the heterogeneous learning environment …
[HTML][HTML] An Efficient Attribute-Based Participant Selecting Scheme with Blockchain for Federated Learning in Smart Cities
X Yin, H Qiu, X Wu, X Zhang - Computers, 2024 - mdpi.com
In smart cities, large amounts of multi-source data are generated all the time. A model
established via machine learning can mine information from these data and enable many …
established via machine learning can mine information from these data and enable many …
Staleness Aware Semi-asynchronous Federated Learning
As the attempts to distribute deep learning using personal data have increased, the
importance of federated learning (FL) has also increased. Attempts have been made to …
importance of federated learning (FL) has also increased. Attempts have been made to …
Asynchronous SGD with Stale Gradient Dynamic Adjustment for Deep Learning Training
Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm,
which speeds up deep learning training and plays an important role in distributed deep …
which speeds up deep learning training and plays an important role in distributed deep …
Effective Federated Graph Matching
Graph matching in the setting of federated learning is still an open problem. This paper
proposes an unsupervised federated graph matching algorithm, UFGM, for inferring …
proposes an unsupervised federated graph matching algorithm, UFGM, for inferring …