Federated learning from pre-trained models: A contrastive learning approach

Y Tan, G Long, J Ma, L Liu, T Zhou… - Advances in neural …, 2022 - proceedings.neurips.cc
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …
被引用次数：115 相关文章
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[PDF][PDF] Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang - opus.lib.uts.edu.au
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou… - Advances in Neural …, 2022 - openreview.net
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated learning from pre-trained models: a contrastive learning approach

Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang - Proceedings of the 36th …, 2022 - dl.acm.org
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang - arXiv preprint arXiv …, 2022 - arxiv.org
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang - First Workshop on Pre-training … - openreview.net
Excessive computation and communication demands pose challenges to current FL
frameworks, especially when training large-scale models. To prevent these issues from …

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Y Tan, G Long, J Ma, L Liu, T Zhou… - Advances in Neural …, 2022 - opus.lib.uts.edu.au
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …