Big-fed: Bilevel optimization enhanced graph-aided federated learning

P Xing, S Lu, L Wu, H Yu - IEEE Transactions on Big Data, 2022 - ieeexplore.ieee.org
In federated learning (FL), due to the non-iid nature of distributedly owned local datasets,
personalization is an important design goal. In this paper, we investigate FL scenarios in …
被引用次数：17 相关文章

BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

P Xing, S Lu, L Wu, H Yu - IEEE Transactions on Big Data, 2022 - computer.org
In federated learning (FL), due to the non-iid nature of distributedly owned local datasets,
personalization is an important design goal. In this paper, we investigate FL scenarios in …

BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

P Xing, S Lu, L Wu, H Yu - IEEE Transactions on Big Data, 2021 - research.ibm.com
In federated learning (FL), due to the non-iid nature of distributedly owned local datasets,
personalization is an important design goal. In this paper, we investigate FL scenarios in …
[PDF] github.io

[PDF][PDF] BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

P Xing, S Lu, L Wu, H Yu - fl-icml.github.io
Federated learning (FL) has become a useful machine learning paradigm for training
models with distributedly owned sensitive data. In this paper, we extend the non …