Decentralized federated meta‐learning framework for few‐shot multitask learning
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22951
Abstract: Federated learning is increasingly attractive, however as the number of training samples on a single device is too small and the training tasks of the devices are different, it faces the few‐shot multitask learning problem.… read more here.
Keywords: multitask; shot multitask; decentralized federated; multitask learning ... See more keywords
Efficient Federated Meta-Learning Over Multi-Access Wireless Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2022.3143259
Abstract: Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today’s edge learning arena. However, its performance is often limited by slow convergence and corresponding low… read more here.
Keywords: tex math; wireless; inline formula; federated meta ... See more keywords