Dynafed: Tackling client data heterogeneity with global dynamics
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
[PDF][PDF] DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim, Q Chen - researchgate.net
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang… - 2023 IEEE/CVF …, 2023 - ieeexplore.ieee.org
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous
client data. Local training on non-iid distributed data results in deflected local optimum …
client data. Local training on non-iid distributed data results in deflected local optimum …
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim… - 2023 IEEE/CVF …, 2023 - computer.org
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim… - arXiv preprint arXiv …, 2022 - arxiv.org
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous
client data. Local training on non-iid distributed data results in deflected local optimum …
client data. Local training on non-iid distributed data results in deflected local optimum …
[PDF][PDF] DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim, Q Chen - xyq7.github.io
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
[PDF][PDF] DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim, Q Chen - cqf.io
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim… - arXiv e …, 2022 - ui.adsabs.harvard.edu
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …
[PDF][PDF] DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim, Q Chen - cqf.io
Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …
heterogeneous client data. Local training on non-iid distributed data results in deflected …