Federated learning from small datasets
Federated learning allows multiple parties to collaboratively train a joint model without
sharing local data. This enables applications of machine learning in settings of inherently …
sharing local data. This enables applications of machine learning in settings of inherently …
Protecting Sensitive Data through Federated Co-Training
In many critical applications, sensitive data is inherently distributed. Federated learning
trains a model collaboratively by aggregating the parameters of locally trained models. This …
trains a model collaboratively by aggregating the parameters of locally trained models. This …
The changing landscape of machine learning: A comparative analysis of centralized machine learning, distributed machine learning and federated machine learning
D Naik, N Naik - UK Workshop on Computational Intelligence, 2023 - Springer
The landscape of machine learning is changing rapidly due to the ever-evolving nature of
data and devices. The large centralized data is replaced by the distributed data and a …
data and devices. The large centralized data is replaced by the distributed data and a …
Federated Binary Matrix Factorization using Proximal Optimization
Identifying informative components in binary data is an essential task in many research
areas, including life sciences, social sciences, and recommendation systems. Boolean …
areas, including life sciences, social sciences, and recommendation systems. Boolean …
Taujud: test augmentation of machine learning in judicial documents
Z Guo, J Liu, T He, Z Li, P Zhangzhu - Proceedings of the 29th ACM …, 2020 - dl.acm.org
The booming of big data makes the adoption of machine learning ubiquitous in the legal
field. As we all know, a large amount of test data can better reflect the performance of the …
field. As we all know, a large amount of test data can better reflect the performance of the …
AIMHI: Protecting sensitive data through federated co-training
Federated learning offers collaborative training among distributed sites without sharing
sensitive local information by sharing the sites' model parameters. It is possible, though, to …
sensitive local information by sharing the sites' model parameters. It is possible, though, to …
Resource-constrained on-device learning by dynamic averaging
The communication between data-generating devices is partially responsible for a growing
portion of the world's power consumption. Thus reducing communication is vital, both, from …
portion of the world's power consumption. Thus reducing communication is vital, both, from …
Picking Daisies in Private: Federated Learning from Small Datasets
Federated learning allows multiple parties to collaboratively train a joint model without
sharing local data. This enables applications of machine learning in settings of inherently …
sharing local data. This enables applications of machine learning in settings of inherently …
Resource-Constrained On-Device Learning by Dynamic Averaging
N Piatkowski, K Morik - … of the European Conference on Machine …, 2021 - books.google.com
The communication between data-generating devices is partially responsible for a growing
portion of the world's power consumption. Thus reducing communication is vital, both, from …
portion of the world's power consumption. Thus reducing communication is vital, both, from …