Knowledge-injected federated learning
Federated learning is an emerging technique for training models from decentralized data
sets. In many applications, data owners participating in the federated learning system hold …
sets. In many applications, data owners participating in the federated learning system hold …
Meta knowledge condensation for federated learning
Existing federated learning paradigms usually extensively exchange distributed models at a
central solver to achieve a more powerful model. However, this would incur severe …
central solver to achieve a more powerful model. However, this would incur severe …
Prototype helps federated learning: Towards faster convergence
Federated learning (FL) is a distributed machine learning technique in which multiple clients
cooperate to train a shared model without exchanging their raw data. However …
cooperate to train a shared model without exchanging their raw data. However …
Federated learning with server learning: Enhancing performance for non-iid data
Federated Learning (FL) has emerged as a means of distributed learning using local data
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
Heterogeneous Federated Learning via Personalized Generative Networks
Federated Learning (FL) allows several clients to construct a common global machine-
learning model without having to share their data. FL, however, faces the challenge of …
learning model without having to share their data. FL, however, faces the challenge of …
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
E Jafarigol, T Trafalis, T Razzaghi… - arXiv preprint arXiv …, 2023 - arxiv.org
In the growing world of artificial intelligence, federated learning is a distributed learning
framework enhanced to preserve the privacy of individuals' data. Federated learning lays the …
framework enhanced to preserve the privacy of individuals' data. Federated learning lays the …
Incentivizing federated learning
Federated Learning is an emerging distributed collaborative learning paradigm used by
many of applications nowadays. The effectiveness of federated learning relies on clients' …
many of applications nowadays. The effectiveness of federated learning relies on clients' …
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Federated learning (FL) is a well established method for performing machine learning tasks
over massively distributed data. However in settings where data is distributed in a non-iid …
over massively distributed data. However in settings where data is distributed in a non-iid …
Parallel distributed logistic regression for vertical federated learning without third-party coordinator
S Yang, B Ren, X Zhou, L Liu - arXiv preprint arXiv:1911.09824, 2019 - arxiv.org
Federated Learning is a new distributed learning mechanism which allows model training
on a large corpus of decentralized data owned by different data providers, without sharing or …
on a large corpus of decentralized data owned by different data providers, without sharing or …
Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Though successful, federated learning presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …