FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation

Fedlc: Accelerating asynchronous federated learning in edge computing

Y Xu, Z Ma, H Xu, S Chen, J Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) has been widely adopted to process the enormous data in the
application scenarios like Edge Computing (EC). However, the commonly-used …
被引用次数：2 相关文章 所有 3 个版本
[PDF] arxiv.org

Fair Concurrent Training of Multiple Models in Federated Learning

M Siew, H Zhang, JI Park, Y Liu, Y Ruan, L Su… - arXiv preprint arXiv …, 2024 - arxiv.org
Federated learning (FL) enables collaborative learning across multiple clients. In most FL
work, all clients train a single learning task. However, the recent proliferation of FL …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios

Z Chen, C Jia, M Hu, X Xie, A Li, M Chen - arXiv preprint arXiv:2407.12729, 2024 - arxiv.org
Along with the increasing popularity of Deep Learning (DL) techniques, more and more
Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable …
相关文章 所有 2 个版本
[PDF] arxiv.org

KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting

Z Xia, M Hu, D Yan, R Liu, A Li, X Xie… - arXiv preprint arXiv …, 2024 - arxiv.org
Although Split Federated Learning (SFL) is good at enabling knowledge sharing among
resource-constrained clients, it suffers from the problem of low training accuracy due to the …
相关文章 所有 2 个版本

Grouped Federated Learning Algorithm Based on Non-IID Data

Z Li, J Zhang - Proceedings of the 2023 4th International Conference …, 2023 - dl.acm.org
Federated learning is a new machine learning paradigm in which multiple clients
collaborate to train a machine learning model while protecting local data privacy. Client-side …
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